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Repository instances, known solution-status markers, and submitted results in one searchable table.
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Have a better bound, a new feasible solution, a quantum run, or a useful negative result? QOBLIB accepts benchmark submissions by pull request using the canonical summary CSV template.
| Instance | Problem | Why benchmark it | Family | Format | Status | Best submitted objective | Submitter | Rows | Source |
|---|---|---|---|---|---|---|---|---|---|
| ms_03_050_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 23 vars, density 0.899, optimal yes; QUBO, 116 vars, density 0.543, optimal yes | .dat | Optimal | -201117 | Daniel Hinderink (hiq-lab) | 3 | Instance Solution Best submission | |
| ms_03_050_005 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 23 vars, density 0.899, optimal yes; QUBO, 116 vars, density 0.543, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_03_050_007 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 23 vars, density 0.855, optimal yes; QUBO, 116 vars, density 0.528, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_03_050_009 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 23 vars, density 0.87, optimal yes; QUBO, 116 vars, density 0.533, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_03_100_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 23 vars, density 0.884, optimal yes; QUBO, 116 vars, density 0.538, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_03_100_012 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 23 vars, density 0.899, optimal yes; QUBO, 116 vars, density 0.543, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_03_100_019 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 23 vars, density 0.913, optimal yes; QUBO, 116 vars, density 0.547, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_03_100_022 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 23 vars, density 0.913, optimal yes; QUBO, 116 vars, density 0.547, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_03_200_050 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 23 vars, density 0.913, optimal yes; QUBO, 116 vars, density 0.547, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_03_200_068 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 23 vars, density 0.913, optimal yes; QUBO, 116 vars, density 0.547, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_03_200_161 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 23 vars, density 0.899, optimal yes; QUBO, 116 vars, density 0.543, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_03_200_177 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 23 vars, density 0.899, optimal yes; QUBO, 116 vars, density 0.543, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_04_050_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 34 vars, density 0.89, optimal yes; QUBO, 158 vars, density 0.503, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_04_050_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 34 vars, density 0.904, optimal yes; QUBO, 158 vars, density 0.508, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_04_050_004 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 34 vars, density 0.89, optimal yes; QUBO, 158 vars, density 0.503, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_04_050_005 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 34 vars, density 0.897, optimal yes; QUBO, 158 vars, density 0.506, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_04_100_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 34 vars, density 0.912, optimal yes; QUBO, 158 vars, density 0.511, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_04_100_009 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 34 vars, density 0.904, optimal yes; QUBO, 158 vars, density 0.508, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_04_100_013 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 34 vars, density 0.904, optimal yes; QUBO, 158 vars, density 0.508, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_04_100_015 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 34 vars, density 0.912, optimal yes; QUBO, 158 vars, density 0.511, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_04_200_030 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 34 vars, density 0.904, optimal yes; QUBO, 158 vars, density 0.508, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_04_200_150 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 34 vars, density 0.904, optimal yes; QUBO, 158 vars, density 0.508, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_04_200_174 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 34 vars, density 0.912, optimal yes; QUBO, 158 vars, density 0.511, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_04_200_176 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 34 vars, density 0.912, optimal yes; QUBO, 158 vars, density 0.511, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_05_050_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 45 vars, density 0.893, optimal yes; QUBO, 200 vars, density 0.484, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_05_050_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 45 vars, density 0.893, optimal yes; QUBO, 200 vars, density 0.484, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_05_050_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 45 vars, density 0.898, optimal yes; QUBO, 200 vars, density 0.486, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_05_050_004 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 45 vars, density 0.88, optimal yes; QUBO, 200 vars, density 0.479, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_05_100_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 45 vars, density 0.911, optimal yes; QUBO, 200 vars, density 0.491, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_05_100_006 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 45 vars, density 0.907, optimal yes; QUBO, 200 vars, density 0.489, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_05_100_013 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 45 vars, density 0.907, optimal yes; QUBO, 200 vars, density 0.489, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_05_100_015 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 45 vars, density 0.911, optimal yes; QUBO, 200 vars, density 0.491, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_05_200_070 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 45 vars, density 0.911, optimal yes; QUBO, 200 vars, density 0.491, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_05_200_095 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 45 vars, density 0.911, optimal yes; QUBO, 200 vars, density 0.491, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_05_200_180 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 45 vars, density 0.911, optimal yes; QUBO, 200 vars, density 0.491, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_05_200_199 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 45 vars, density 0.907, optimal yes; QUBO, 200 vars, density 0.489, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_06_050_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 56 vars, density 0.884, optimal yes; QUBO, 242 vars, density 0.468, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_06_050_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 56 vars, density 0.899, optimal yes; QUBO, 242 vars, density 0.473, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_06_050_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 56 vars, density 0.899, optimal yes; QUBO, 242 vars, density 0.473, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_06_050_004 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 56 vars, density 0.89, optimal yes; QUBO, 242 vars, density 0.47, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_06_100_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 56 vars, density 0.902, optimal yes; QUBO, 242 vars, density 0.474, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_06_100_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 56 vars, density 0.911, optimal yes; QUBO, 242 vars, density 0.478, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_06_100_005 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 56 vars, density 0.893, optimal yes; QUBO, 242 vars, density 0.471, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_06_100_010 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 56 vars, density 0.905, optimal yes; QUBO, 242 vars, density 0.475, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_06_200_077 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 56 vars, density 0.902, optimal yes; QUBO, 242 vars, density 0.474, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_06_200_104 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 56 vars, density 0.911, optimal yes; QUBO, 242 vars, density 0.478, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_06_200_240 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 56 vars, density 0.908, optimal yes; QUBO, 242 vars, density 0.477, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_06_200_289 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 56 vars, density 0.908, optimal yes; QUBO, 242 vars, density 0.477, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_07_050_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 67 vars, density 0.881, optimal yes; QUBO, 284 vars, density 0.458, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_07_050_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 67 vars, density 0.902, optimal yes; QUBO, 284 vars, density 0.465, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_07_050_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 67 vars, density 0.9, optimal yes; QUBO, 284 vars, density 0.465, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_07_050_004 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 67 vars, density 0.893, optimal yes; QUBO, 284 vars, density 0.462, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_07_100_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 67 vars, density 0.904, optimal yes; QUBO, 284 vars, density 0.466, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_07_100_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 67 vars, density 0.91, optimal yes; QUBO, 284 vars, density 0.469, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_07_100_005 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 67 vars, density 0.891, optimal yes; QUBO, 284 vars, density 0.462, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_07_100_006 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 67 vars, density 0.902, optimal yes; QUBO, 284 vars, density 0.465, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_07_200_248 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 67 vars, density 0.906, optimal yes; QUBO, 284 vars, density 0.467, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_07_200_370 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 67 vars, density 0.904, optimal yes; QUBO, 284 vars, density 0.466, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_07_200_398 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 67 vars, density 0.908, optimal yes; QUBO, 284 vars, density 0.468, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_07_200_500 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 67 vars, density 0.904, optimal yes; QUBO, 284 vars, density 0.466, optimal yes | .dat | Optimal | 0.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ms_08_050_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 78 vars, density 0.891, optimal yes; QUBO, 326 vars, density 0.455, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_08_050_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 78 vars, density 0.889, optimal yes; QUBO, 326 vars, density 0.454, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_08_050_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 78 vars, density 0.891, optimal yes; QUBO, 326 vars, density 0.455, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_08_050_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 78 vars, density 0.896, optimal yes; QUBO, 326 vars, density 0.457, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_08_100_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 78 vars, density 0.904, optimal yes; QUBO, 326 vars, density 0.46, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_08_100_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 78 vars, density 0.897, optimal yes; QUBO, 326 vars, density 0.457, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_08_100_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 78 vars, density 0.904, optimal yes; QUBO, 326 vars, density 0.46, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_08_100_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 78 vars, density 0.902, optimal yes; QUBO, 326 vars, density 0.459, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_08_200_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 78 vars, density 0.907, optimal yes; QUBO, 326 vars, density 0.461, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_08_200_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 78 vars, density 0.907, optimal yes; QUBO, 326 vars, density 0.461, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_08_200_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 78 vars, density 0.907, optimal yes; QUBO, 326 vars, density 0.461, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_08_200_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 78 vars, density 0.902, optimal yes; QUBO, 326 vars, density 0.459, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_09_050_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 89 vars, density 0.891, optimal yes; QUBO, 368 vars, density 0.45, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_09_050_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 89 vars, density 0.89, optimal yes; QUBO, 368 vars, density 0.45, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_09_050_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 89 vars, density 0.89, optimal yes; QUBO, 368 vars, density 0.45, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_09_050_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 89 vars, density 0.891, optimal yes; QUBO, 368 vars, density 0.45, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_09_100_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 89 vars, density 0.903, optimal yes; QUBO, 368 vars, density 0.454, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_09_100_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 89 vars, density 0.898, optimal yes; QUBO, 368 vars, density 0.452, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_09_100_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 89 vars, density 0.904, optimal yes; QUBO, 368 vars, density 0.455, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_09_100_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 89 vars, density 0.898, optimal yes; QUBO, 368 vars, density 0.452, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_09_200_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 89 vars, density 0.908, optimal yes; QUBO, 368 vars, density 0.456, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_09_200_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 89 vars, density 0.908, optimal yes; QUBO, 368 vars, density 0.456, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_09_200_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 89 vars, density 0.908, optimal yes; QUBO, 368 vars, density 0.456, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_09_200_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 89 vars, density 0.901, optimal yes; QUBO, 368 vars, density 0.454, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_10_050_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 100 vars, density 0.892, optimal yes; QUBO, 410 vars, density 0.446, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_10_050_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 100 vars, density 0.888, optimal yes; QUBO, 410 vars, density 0.445, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_10_050_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 100 vars, density 0.892, optimal yes; QUBO, 410 vars, density 0.446, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_10_050_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 100 vars, density 0.89, optimal yes; QUBO, 410 vars, density 0.445, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_10_100_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 100 vars, density 0.902, optimal yes; QUBO, 410 vars, density 0.45, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_10_100_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 100 vars, density 0.896, optimal yes; QUBO, 410 vars, density 0.448, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_10_100_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 100 vars, density 0.903, optimal yes; QUBO, 410 vars, density 0.45, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_10_100_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 100 vars, density 0.898, optimal yes; QUBO, 410 vars, density 0.449, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_10_200_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 100 vars, density 0.908, optimal yes; QUBO, 410 vars, density 0.452, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_10_200_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 100 vars, density 0.906, optimal yes; QUBO, 410 vars, density 0.452, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_10_200_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 100 vars, density 0.908, optimal yes; QUBO, 410 vars, density 0.452, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_10_200_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 100 vars, density 0.902, optimal yes; QUBO, 410 vars, density 0.45, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_11_050_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 111 vars, density 0.894, optimal yes; QUBO, 452 vars, density 0.444, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_11_050_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 111 vars, density 0.89, optimal yes; QUBO, 452 vars, density 0.442, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_11_050_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 111 vars, density 0.895, optimal yes; QUBO, 452 vars, density 0.444, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_11_050_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 111 vars, density 0.888, optimal yes; QUBO, 452 vars, density 0.441, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_11_100_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 111 vars, density 0.901, optimal yes; QUBO, 452 vars, density 0.446, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_11_100_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 111 vars, density 0.897, optimal yes; QUBO, 452 vars, density 0.445, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_11_100_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 111 vars, density 0.904, optimal yes; QUBO, 452 vars, density 0.448, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_11_100_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 111 vars, density 0.897, optimal yes; QUBO, 452 vars, density 0.445, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_11_200_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 111 vars, density 0.906, optimal yes; QUBO, 452 vars, density 0.448, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_11_200_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 111 vars, density 0.906, optimal yes; QUBO, 452 vars, density 0.448, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_11_200_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 111 vars, density 0.908, optimal yes; QUBO, 452 vars, density 0.449, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_11_200_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 111 vars, density 0.902, optimal yes; QUBO, 452 vars, density 0.447, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_12_050_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 122 vars, density 0.893, optimal yes; QUBO, 494 vars, density 0.441, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_12_050_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 122 vars, density 0.889, optimal yes; QUBO, 494 vars, density 0.439, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_12_050_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 122 vars, density 0.893, optimal yes; QUBO, 494 vars, density 0.441, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_12_050_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 122 vars, density 0.887, optimal yes; QUBO, 494 vars, density 0.439, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_12_100_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 122 vars, density 0.901, optimal yes; QUBO, 494 vars, density 0.444, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_12_100_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 122 vars, density 0.896, optimal yes; QUBO, 494 vars, density 0.442, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_12_100_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 122 vars, density 0.903, optimal yes; QUBO, 494 vars, density 0.445, optimal yes | .dat | Optimal | 0 | Instance Solution | |||
| ms_12_100_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 122 vars, density 0.897, optimal yes; QUBO, 494 vars, density 0.442, optimal yes | .dat | Open | 0 | Instance | |||
| ms_12_200_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 122 vars, density 0.906, optimal yes; QUBO, 494 vars, density 0.446, optimal yes | .dat | Open | 0 | Instance | |||
| ms_12_200_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 122 vars, density 0.905, optimal yes; QUBO, 494 vars, density 0.445, optimal yes | .dat | Open | 0 | Instance | |||
| ms_12_200_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 122 vars, density 0.906, optimal yes; QUBO, 494 vars, density 0.446, optimal yes | .dat | Open | 0 | Instance | |||
| ms_12_200_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 122 vars, density 0.902, optimal yes; QUBO, 494 vars, density 0.444, optimal yes | .dat | Open | 0 | Instance | |||
| ms_13_050_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 133 vars, density 0.893, optimal yes; QUBO, 536 vars, density 0.439, optimal yes | .dat | Open | 0 | Instance | |||
| ms_13_050_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 133 vars, density 0.89, optimal yes; QUBO, 536 vars, density 0.437, optimal yes | .dat | Open | 0 | Instance | |||
| ms_13_050_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 133 vars, density 0.891, optimal yes; QUBO, 536 vars, density 0.438, optimal yes | .dat | Open | 0 | Instance | |||
| ms_13_050_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 133 vars, density 0.889, optimal yes; QUBO, 536 vars, density 0.437, optimal yes | .dat | Open | 0 | Instance | |||
| ms_13_100_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 133 vars, density 0.901, optimal yes; QUBO, 536 vars, density 0.441, optimal yes | .dat | Open | 0 | Instance | |||
| ms_13_100_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 133 vars, density 0.898, optimal yes; QUBO, 536 vars, density 0.44, optimal yes | .dat | Open | 0 | Instance | |||
| ms_13_100_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 133 vars, density 0.902, optimal yes; QUBO, 536 vars, density 0.442, optimal yes | .dat | Open | 0 | Instance | |||
| ms_13_100_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 133 vars, density 0.898, optimal yes; QUBO, 536 vars, density 0.441, optimal yes | .dat | Open | 0 | Instance | |||
| ms_13_200_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 133 vars, density 0.906, optimal yes; QUBO, 536 vars, density 0.443, optimal yes | .dat | Open | 0 | Instance | |||
| ms_13_200_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 133 vars, density 0.906, optimal yes; QUBO, 536 vars, density 0.443, optimal yes | .dat | Open | 0 | Instance | |||
| ms_13_200_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 133 vars, density 0.906, optimal yes; QUBO, 536 vars, density 0.443, optimal yes | .dat | Open | 0 | Instance | |||
| ms_13_200_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 133 vars, density 0.902, optimal yes; QUBO, 536 vars, density 0.442, optimal yes | .dat | Open | 0 | Instance | |||
| ms_14_050_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 144 vars, density 0.892, optimal yes; QUBO, 578 vars, density 0.436, optimal yes | .dat | Open | 0 | Instance | |||
| ms_14_050_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 144 vars, density 0.89, optimal yes; QUBO, 578 vars, density 0.435, optimal yes | .dat | Open | 0 | Instance | |||
| ms_14_050_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 144 vars, density 0.89, optimal yes; QUBO, 578 vars, density 0.435, optimal yes | .dat | Open | 0 | Instance | |||
| ms_14_050_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 144 vars, density 0.89, optimal yes; QUBO, 578 vars, density 0.436, optimal yes | .dat | Open | 0 | Instance | |||
| ms_14_100_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 144 vars, density 0.9, optimal yes; QUBO, 578 vars, density 0.439, optimal yes | .dat | Open | 0 | Instance | |||
| ms_14_100_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 144 vars, density 0.899, optimal yes; QUBO, 578 vars, density 0.439, optimal yes | .dat | Open | 0 | Instance | |||
| ms_14_100_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 144 vars, density 0.901, optimal yes; QUBO, 578 vars, density 0.44, optimal yes | .dat | Open | 0 | Instance | |||
| ms_14_100_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 144 vars, density 0.899, optimal yes; QUBO, 578 vars, density 0.439, optimal yes | .dat | Open | 0 | Instance | |||
| ms_14_200_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 144 vars, density 0.905, optimal yes; QUBO, 578 vars, density 0.441, optimal yes | .dat | Open | 0 | Instance | |||
| ms_14_200_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 144 vars, density 0.906, optimal yes; QUBO, 578 vars, density 0.442, optimal yes | .dat | Open | 0 | Instance | |||
| ms_14_200_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 144 vars, density 0.904, optimal yes; QUBO, 578 vars, density 0.441, optimal yes | .dat | Open | 0 | Instance | |||
| ms_14_200_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 144 vars, density 0.903, optimal yes; QUBO, 578 vars, density 0.44, optimal yes | .dat | Open | 0 | Instance | |||
| ms_15_050_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 155 vars, density 0.893, optimal yes; QUBO, 620 vars, density 0.435, optimal yes | .dat | Open | 0 | Instance | |||
| ms_15_050_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 155 vars, density 0.891, optimal yes; QUBO, 620 vars, density 0.434, optimal yes | .dat | Open | 0 | Instance | |||
| ms_15_050_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 155 vars, density 0.889, optimal yes; QUBO, 620 vars, density 0.434, optimal yes | .dat | Open | 0 | Instance | |||
| ms_15_050_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 155 vars, density 0.889, optimal yes; QUBO, 620 vars, density 0.434, optimal yes | .dat | Open | 0 | Instance | |||
| ms_15_100_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 155 vars, density 0.901, optimal yes; QUBO, 620 vars, density 0.438, optimal yes | .dat | Open | 0 | Instance | |||
| ms_15_100_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 155 vars, density 0.9, optimal yes; QUBO, 620 vars, density 0.438, optimal yes | .dat | Open | 0 | Instance | |||
| ms_15_100_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 155 vars, density 0.899, optimal yes; QUBO, 620 vars, density 0.437, optimal yes | .dat | Open | 0 | Instance | |||
| ms_15_100_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 155 vars, density 0.898, optimal yes; QUBO, 620 vars, density 0.437, optimal yes | .dat | Open | 0 | Instance | |||
| ms_15_200_000 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 155 vars, density 0.905, optimal yes; QUBO, 620 vars, density 0.44, optimal yes | .dat | Open | 0 | Instance | |||
| ms_15_200_001 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 155 vars, density 0.906, optimal yes; QUBO, 620 vars, density 0.44, optimal yes | .dat | Open | 0 | Instance | |||
| ms_15_200_002 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 155 vars, density 0.903, optimal yes; QUBO, 620 vars, density 0.439, optimal yes | .dat | Open | 0 | Instance | |||
| ms_15_200_003 | Market Split | A compact feasibility benchmark for partitioning integer data across many simultaneous subset-sum constraints.Paper: MIP, 155 vars, density 0.903, optimal yes; QUBO, 620 vars, density 0.439, optimal yes | .dat | Open | 0 | Instance | |||
| LABS (N = 10) | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones. | Best known | 13 | Angelika Widl (Math.Tec), Daniel Egger (IBM), Juris Ulmanis (AQT), Christoph Regner (Math.Tec) | 2 | Best submission | ||
| LABS (N = 12) | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones. | Best known | 10 | Angelika Widl (Math.Tec), Daniel Egger (IBM), Juris Ulmanis (AQT), Christoph Regner (Math.Tec) | 2 | Best submission | ||
| LABS (N = 6) | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones. | Best known | 7 | Angelika Widl (Math.Tec), Daniel Egger (IBM), Juris Ulmanis (AQT), Christoph Regner (Math.Tec) | 2 | Best submission | ||
| LABS (N = 8) | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones. | Best known | 8 | Angelika Widl (Math.Tec), Daniel Egger (IBM), Juris Ulmanis (AQT), Christoph Regner (Math.Tec) | 2 | Best submission | ||
| labs002 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 4 vars, density 0.214, optimal yes; QUBO, 3 vars, density 0.667, optimal yes | Optimal | 1 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs003 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 6 vars, density 0.16, optimal yes; QUBO, 6 vars, density 0.81, optimal yes | Optimal | 1 | Daniel Hinderink (hiq-lab) | 3 | Solution Best submission | ||
| labs004 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 8 vars, density 0.131, optimal yes; QUBO, 10 vars, density 0.764, optimal yes | Optimal | 2 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs005 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 10 vars, density 0.108, optimal yes; QUBO, 15 vars, density 0.667, optimal yes | Optimal | 2 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs006 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 12 vars, density 0.0926, optimal yes; QUBO, 21 vars, density 0.597, optimal yes | Optimal | 7 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs007 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 14 vars, density 0.0804, optimal yes; QUBO, 28 vars, density 0.534, optimal yes | Optimal | 3 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs008 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 16 vars, density 0.0715, optimal yes; QUBO, 36 vars, density 0.486, optimal yes | Optimal | 8 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs009 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 18 vars, density 0.0641, optimal yes; QUBO, 45 vars, density 0.443, optimal yes | Optimal | 12 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs010 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 20 vars, density 0.0583, optimal yes; QUBO, 55 vars, density 0.409, optimal yes | Optimal | 13 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs011 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 22 vars, density 0.0532, optimal yes; QUBO, 66 vars, density 0.378, optimal yes | Optimal | 5 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs012 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 24 vars, density 0.0491, optimal yes; QUBO, 78 vars, density 0.352, optimal yes | Optimal | 10 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs013 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 26 vars, density 0.0455, optimal yes; QUBO, 91 vars, density 0.329, optimal yes | Optimal | 6 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs014 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 28 vars, density 0.0425, optimal yes; QUBO, 105 vars, density 0.309, optimal yes | Optimal | 19 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs015 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 30 vars, density 0.0397, optimal yes; QUBO, 120 vars, density 0.291, optimal yes | Optimal | 15 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs016 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 32 vars, density 0.0374, optimal yes; QUBO, 136 vars, density 0.276, optimal yes | Optimal | 24 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs017 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 34 vars, density 0.0353, optimal yes; QUBO, 153 vars, density 0.261, optimal yes | Optimal | 32 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs018 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 36 vars, density 0.0334, optimal yes; QUBO, 171 vars, density 0.248, optimal yes | Optimal | 25 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs019 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 38 vars, density 0.0317, optimal yes; QUBO, 190 vars, density 0.237, optimal yes | Optimal | 29 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs020 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 40 vars, density 0.0302, optimal yes; QUBO, 210 vars, density 0.226, optimal yes | Optimal | 26 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs021 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 42 vars, density 0.0288, optimal yes; QUBO, 231 vars, density 0.216, optimal yes | Optimal | 26 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs022 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 44 vars, density 0.0275, optimal yes; QUBO, 253 vars, density 0.207, optimal yes | Optimal | 39 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs023 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 46 vars, density 0.0263, optimal yes; QUBO, 276 vars, density 0.199, optimal yes | Optimal | 47 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs024 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 48 vars, density 0.0253, optimal yes; QUBO, 300 vars, density 0.192, optimal yes | Optimal | 36 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs025 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 50 vars, density 0.0243, optimal yes; QUBO, 325 vars, density 0.185, optimal yes | Optimal | 36 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs026 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 52 vars, density 0.0234, optimal yes; QUBO, 351 vars, density 0.178, optimal yes | Optimal | 45 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs027 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 54 vars, density 0.0225, optimal yes; QUBO, 378 vars, density 0.172, optimal yes | Optimal | 37.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs028 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 56 vars, density 0.0218, optimal yes; QUBO, 406 vars, density 0.166, optimal yes | Optimal | 50.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs029 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 58 vars, density 0.021, optimal yes; QUBO, 435 vars, density 0.161, optimal yes | Optimal | 62.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs030 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 60 vars, density 0.0204, optimal yes; QUBO, 465 vars, density 0.156, optimal yes | Optimal | 59 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs031 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 62 vars, density 0.0197, optimal yes; QUBO, 496 vars, density 0.151, optimal yes | Optimal | 67.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs032 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 64 vars, density 0.0191, optimal yes; QUBO, 528 vars, density 0.147, optimal yes | Optimal | 64.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs033 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 66 vars, density 0.0185, optimal yes; QUBO, 561 vars, density 0.143, optimal yes | Optimal | 64.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs034 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 68 vars, density 0.018, optimal yes; QUBO, 595 vars, density 0.139, optimal yes | Optimal | 65.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs035 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 70 vars, density 0.0175, optimal yes; QUBO, 630 vars, density 0.135, optimal yes | Optimal | 73.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs036 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 72 vars, density 0.017, optimal yes; QUBO, 666 vars, density 0.131, optimal yes | Optimal | 82.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs037 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 74 vars, density 0.0166, optimal yes; QUBO, 703 vars, density 0.128, optimal yes | Optimal | 86.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs038 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 76 vars, density 0.0161, optimal yes; QUBO, 741 vars, density 0.125, optimal yes | Optimal | 87.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs039 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 78 vars, density 0.0157, optimal yes; QUBO, 780 vars, density 0.122, optimal yes | Optimal | 99.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs040 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 80 vars, density 0.0154, optimal yes; QUBO, 820 vars, density 0.119, optimal yes | Optimal | 116.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs041 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 82 vars, density 0.015, optimal no; QUBO, 861 vars, density 0.116, optimal no | Best known | 112.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs042 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 84 vars, density 0.0146, optimal no; QUBO, 903 vars, density 0.114, optimal no | Best known | 117.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs043 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 86 vars, density 0.0143, optimal no; QUBO, 946 vars, density 0.111, optimal no | Best known | 137.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs044 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 88 vars, density 0.014, optimal no; QUBO, 990 vars, density 0.109, optimal no | Best known | 146.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs045 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 90 vars, density 0.0137, optimal no; QUBO, 1035 vars, density 0.106, optimal no | Best known | 154.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs046 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 92 vars, density 0.0134, optimal no; QUBO, 1081 vars, density 0.104, optimal no | Best known | 179.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs047 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 94 vars, density 0.0131, optimal no; QUBO, 1128 vars, density 0.102, optimal no | Best known | 175.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs048 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 96 vars, density 0.0128, optimal no; QUBO, 1176 vars, density 0.0999, optimal no | Best known | 196 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs049 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 98 vars, density 0.0126, optimal no; QUBO, 1225 vars, density 0.0979, optimal no | Best known | 216 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs050 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 100 vars, density 0.0123, optimal no; QUBO, 1275 vars, density 0.0961, optimal no | Best known | 225.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs051 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 102 vars, density 0.0121, optimal no; QUBO, 1326 vars, density 0.0943, optimal no | Best known | 245.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs052 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 104 vars, density 0.0119, optimal no; QUBO, 1378 vars, density 0.0925, optimal no | Best known | 214.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs053 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 106 vars, density 0.0116, optimal no; QUBO, 1431 vars, density 0.0908, optimal no | Best known | 270 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs054 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 108 vars, density 0.0114, optimal no; QUBO, 1485 vars, density 0.0892, optimal no | Best known | 255 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs055 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 110 vars, density 0.0112, optimal no; QUBO, 1540 vars, density 0.0877, optimal no | Best known | 271.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs056 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 112 vars, density 0.011, optimal no; QUBO, 1596 vars, density 0.0861, optimal no | Best known | 252.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs057 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 114 vars, density 0.0108, optimal no; QUBO, 1653 vars, density 0.0847, optimal no | Best known | 320 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs058 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 116 vars, density 0.0106, optimal no; QUBO, 1711 vars, density 0.0833, optimal no | Best known | 317 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs059 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 118 vars, density 0.0105, optimal no; QUBO, 1770 vars, density 0.0819, optimal no | Best known | 333 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs060 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 120 vars, density 0.0103, optimal no; QUBO, 1830 vars, density 0.0806, optimal no | Best known | 326.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs061 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 122 vars, density 0.0101, optimal no; QUBO, 1891 vars, density 0.0793, optimal no | Best known | 346 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs062 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 124 vars, density 0.00997, optimal no; QUBO, 1953 vars, density 0.0781, optimal no | Best known | 379 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs063 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 126 vars, density 0.00981, optimal no; QUBO, 2016 vars, density 0.0769, optimal no | Best known | 387 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs064 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 128 vars, density 0.00966, optimal no; QUBO, 2080 vars, density 0.0757, optimal no | Best known | 412 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs065 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 130 vars, density 0.00951, optimal no; QUBO, 2145 vars, density 0.0746, optimal no | Best known | 368.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs066 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 132 vars, density 0.00937, optimal no; QUBO, 2211 vars, density 0.0735, optimal no | Best known | 417 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs067 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 134 vars, density 0.00923, optimal no; QUBO, 2278 vars, density 0.0724, optimal no | Best known | 457 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs068 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 136 vars, density 0.0091, optimal no; QUBO, 2346 vars, density 0.0714, optimal no | Best known | 402 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs069 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 138 vars, density 0.00897, optimal no; QUBO, 2415 vars, density 0.0704, optimal no | Best known | 446 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs070 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 140 vars, density 0.00884, optimal no; QUBO, 2485 vars, density 0.0694, optimal no | Best known | 527 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs071 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 142 vars, density 0.00872, optimal no; QUBO, 2556 vars, density 0.0685, optimal no | Best known | 499 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs072 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 144 vars, density 0.0086, optimal no; QUBO, 2628 vars, density 0.0675, optimal no | Best known | 492.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs073 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 146 vars, density 0.00848, optimal no; QUBO, 2701 vars, density 0.0666, optimal no | Best known | 548.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs074 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 148 vars, density 0.00837, optimal no; QUBO, 2775 vars, density 0.0658, optimal no | Best known | 557 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs075 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 150 vars, density 0.00826, optimal no; QUBO, 2850 vars, density 0.0649, optimal no | Best known | 553 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs076 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 152 vars, density 0.00815, optimal no; QUBO, 2926 vars, density 0.0641, optimal no | Best known | 566.0 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs077 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 154 vars, density 0.00804, optimal no; QUBO, 3003 vars, density 0.0633, optimal no | Best known | 610 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs078 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 156 vars, density 0.00794, optimal no; QUBO, 3081 vars, density 0.0625, optimal no | Best known | 643 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs079 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 158 vars, density 0.00784, optimal no; QUBO, 3160 vars, density 0.0617, optimal no | Best known | 611 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs080 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 160 vars, density 0.00774, optimal no; QUBO, 3240 vars, density 0.061, optimal no | Best known | 644 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs081 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 162 vars, density 0.00765, optimal no; QUBO, 3321 vars, density 0.0602, optimal no | Best known | 616 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs082 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 164 vars, density 0.00756, optimal no; QUBO, 3403 vars, density 0.0595, optimal no | Best known | 665 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs083 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 166 vars, density 0.00747, optimal no; QUBO, 3486 vars, density 0.0588, optimal no | Best known | 701 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs084 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 168 vars, density 0.00738, optimal no; QUBO, 3570 vars, density 0.0581, optimal no | Best known | 714 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs085 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 170 vars, density 0.00729, optimal no; QUBO, 3655 vars, density 0.0575, optimal no | Best known | 786 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs086 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 172 vars, density 0.00721, optimal no; QUBO, 3741 vars, density 0.0568, optimal no | Best known | 831 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs087 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 174 vars, density 0.00713, optimal no; QUBO, 3828 vars, density 0.0562, optimal no | Best known | 815 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs088 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 176 vars, density 0.00705, optimal no; QUBO, 3916 vars, density 0.0555, optimal no | Best known | 748 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs089 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 178 vars, density 0.00697, optimal no; QUBO, 4005 vars, density 0.0549, optimal no | Best known | 844 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs090 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 180 vars, density 0.00689, optimal no; QUBO, 4095 vars, density 0.0543, optimal no | Best known | 829 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs091 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 182 vars, density 0.00682, optimal no; QUBO, 4186 vars, density 0.0537, optimal no | Best known | 913 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs092 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 184 vars, density 0.00674, optimal no; QUBO, 4278 vars, density 0.0532, optimal no | Best known | 818 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs093 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 186 vars, density 0.00667, optimal no; QUBO, 4371 vars, density 0.0526, optimal no | Best known | 898 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs094 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 188 vars, density 0.0066, optimal no; QUBO, 4465 vars, density 0.0521, optimal no | Best known | 931 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs095 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 190 vars, density 0.00653, optimal no; QUBO, 4560 vars, density 0.0515, optimal no | Best known | 967 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs096 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 192 vars, density 0.00646, optimal no; QUBO, 4656 vars, density 0.051, optimal no | Best known | 1012 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs097 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 194 vars, density 0.0064, optimal no; QUBO, 4753 vars, density 0.0505, optimal no | Best known | 988 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs098 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 196 vars, density 0.00633, optimal no; QUBO, 4851 vars, density 0.05, optimal no | Best known | 1049 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs099 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 198 vars, density 0.00627, optimal no; QUBO, 4950 vars, density 0.0495, optimal no | Best known | 1009 | Maximilian Schicker | 2 | Solution Best submission | ||
| labs100 | LABS | A sequence-design benchmark where small objective changes can separate strong search methods from weak ones.Paper: MIP, 200 vars, density 0.00621, optimal no; QUBO, 5050 vars, density 0.049, optimal no | Best known | 1050 | Maximilian Schicker | 2 | Solution Best submission | ||
| B3_3_1 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 12 vars, density 0.188, optimal yes; QUBO, 126 vars, density 0.361, optimal yes | Optimal | 2.0 | Maximilian Schicker | 1 | Best submission | ||
| B3_3_10 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 12 vars, density 0.188, optimal yes; QUBO, 126 vars, density 0.361, optimal yes | Optimal | 3.0 | Maximilian Schicker | 1 | Best submission | ||
| B3_3_2 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 12 vars, density 0.188, optimal yes; QUBO, 126 vars, density 0.361, optimal yes | Optimal | 3.0 | Maximilian Schicker | 1 | Best submission | ||
| B3_3_3 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 12 vars, density 0.188, optimal yes; QUBO, 126 vars, density 0.361, optimal yes | Optimal | 3.0 | Maximilian Schicker | 1 | Best submission | ||
| B3_3_4 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 12 vars, density 0.188, optimal yes; QUBO, 126 vars, density 0.361, optimal yes | Optimal | 3.0 | Maximilian Schicker | 1 | Best submission | ||
| B3_3_5 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 12 vars, density 0.188, optimal yes; QUBO, 126 vars, density 0.361, optimal yes | Optimal | 3.0 | Maximilian Schicker | 1 | Best submission | ||
| B3_3_6 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 12 vars, density 0.188, optimal yes; QUBO, 126 vars, density 0.361, optimal yes | Optimal | 3.0 | Maximilian Schicker | 1 | Best submission | ||
| B3_3_7 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 12 vars, density 0.188, optimal yes; QUBO, 126 vars, density 0.361, optimal yes | Optimal | 2.0 | Maximilian Schicker | 1 | Best submission | ||
| B3_3_8 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 12 vars, density 0.188, optimal yes; QUBO, 126 vars, density 0.361, optimal yes | Optimal | 3.0 | Maximilian Schicker | 1 | Best submission | ||
| B3_3_9 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 12 vars, density 0.188, optimal yes; QUBO, 126 vars, density 0.361, optimal yes | Optimal | 2.0 | Maximilian Schicker | 1 | Best submission | ||
| B3_9_1 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 12 vars, density 0.188, optimal yes; QUBO, 126 vars, density 0.361, optimal yes | Optimal | 5.0 | Maximilian Schicker | 1 | Best submission | ||
| B3_9_10 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 12 vars, density 0.188, optimal yes; QUBO, 126 vars, density 0.361, optimal yes | Optimal | 4.0 | Maximilian Schicker | 1 | Best submission | ||
| B3_9_2 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 12 vars, density 0.188, optimal yes; QUBO, 126 vars, density 0.361, optimal yes | Optimal | 5.0 | Maximilian Schicker | 1 | Best submission | ||
| B3_9_3 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 12 vars, density 0.188, optimal yes; QUBO, 126 vars, density 0.361, optimal yes | Optimal | 5.0 | Maximilian Schicker | 1 | Best submission | ||
| B3_9_4 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 12 vars, density 0.188, optimal yes; QUBO, 126 vars, density 0.361, optimal yes | Optimal | 5.0 | Maximilian Schicker | 1 | Best submission | ||
| B3_9_5 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 12 vars, density 0.188, optimal yes; QUBO, 126 vars, density 0.361, optimal yes | Optimal | 4.0 | Maximilian Schicker | 1 | Best submission | ||
| B3_9_6 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 12 vars, density 0.188, optimal yes; QUBO, 126 vars, density 0.361, optimal yes | Optimal | 5.0 | Maximilian Schicker | 1 | Best submission | ||
| B3_9_7 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 12 vars, density 0.188, optimal yes; QUBO, 126 vars, density 0.361, optimal yes | Optimal | 4.0 | Maximilian Schicker | 1 | Best submission | ||
| B3_9_8 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 12 vars, density 0.188, optimal yes; QUBO, 126 vars, density 0.361, optimal yes | Optimal | 4.0 | Maximilian Schicker | 1 | Best submission | ||
| B3_9_9 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 12 vars, density 0.188, optimal yes; QUBO, 126 vars, density 0.361, optimal yes | Optimal | 4.0 | Maximilian Schicker | 1 | Best submission | ||
| B4_16_1 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 48 vars, density 0.0854, optimal yes; QUBO, 696 vars, density 0.266, optimal yes | Optimal | 9.0 | Maximilian Schicker | 1 | Best submission | ||
| B4_16_10 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 48 vars, density 0.0854, optimal yes; QUBO, 696 vars, density 0.266, optimal yes | Optimal | 9.0 | Maximilian Schicker | 1 | Best submission | ||
| B4_16_2 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 48 vars, density 0.0854, optimal yes; QUBO, 696 vars, density 0.266, optimal yes | Optimal | 10.0 | Maximilian Schicker | 1 | Best submission | ||
| B4_16_3 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 48 vars, density 0.0854, optimal yes; QUBO, 696 vars, density 0.266, optimal yes | Optimal | 8.0 | Maximilian Schicker | 1 | Best submission | ||
| B4_16_4 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 48 vars, density 0.0854, optimal yes; QUBO, 696 vars, density 0.266, optimal yes | Optimal | 10.0 | Maximilian Schicker | 1 | Best submission | ||
| B4_16_5 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 48 vars, density 0.0854, optimal yes; QUBO, 696 vars, density 0.266, optimal yes | Optimal | 10.0 | Maximilian Schicker | 1 | Best submission | ||
| B4_16_6 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 48 vars, density 0.0854, optimal yes; QUBO, 696 vars, density 0.266, optimal yes | Optimal | 10.0 | Maximilian Schicker | 1 | Best submission | ||
| B4_16_7 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 48 vars, density 0.0854, optimal yes; QUBO, 696 vars, density 0.266, optimal yes | Optimal | 10.0 | Maximilian Schicker | 1 | Best submission | ||
| B4_16_8 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 48 vars, density 0.0854, optimal yes; QUBO, 696 vars, density 0.266, optimal yes | Optimal | 10.0 | Maximilian Schicker | 1 | Best submission | ||
| B4_16_9 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 48 vars, density 0.0854, optimal yes; QUBO, 696 vars, density 0.266, optimal yes | Optimal | 10.0 | Maximilian Schicker | 1 | Best submission | ||
| B4_4_1 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 48 vars, density 0.0854, optimal yes; QUBO, 696 vars, density 0.266, optimal yes | Optimal | 4.0 | Maximilian Schicker | 1 | Best submission | ||
| B4_4_10 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 48 vars, density 0.0854, optimal yes; QUBO, 696 vars, density 0.266, optimal yes | Optimal | 4.0 | Maximilian Schicker | 1 | Best submission | ||
| B4_4_2 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 48 vars, density 0.0854, optimal yes; QUBO, 696 vars, density 0.266, optimal yes | Optimal | 4.0 | Maximilian Schicker | 1 | Best submission | ||
| B4_4_3 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 48 vars, density 0.0854, optimal yes; QUBO, 696 vars, density 0.266, optimal yes | Optimal | 3.0 | Maximilian Schicker | 1 | Best submission | ||
| B4_4_4 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 48 vars, density 0.0854, optimal yes; QUBO, 696 vars, density 0.266, optimal yes | Optimal | 3.0 | Maximilian Schicker | 1 | Best submission | ||
| B4_4_5 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 48 vars, density 0.0854, optimal yes; QUBO, 696 vars, density 0.266, optimal yes | Optimal | 4.0 | Maximilian Schicker | 1 | Best submission | ||
| B4_4_6 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 48 vars, density 0.0854, optimal yes; QUBO, 696 vars, density 0.266, optimal yes | Optimal | 4.0 | Maximilian Schicker | 1 | Best submission | ||
| B4_4_7 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 48 vars, density 0.0854, optimal yes; QUBO, 696 vars, density 0.266, optimal yes | Optimal | 4.0 | Maximilian Schicker | 1 | Best submission | ||
| B4_4_8 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 48 vars, density 0.0854, optimal yes; QUBO, 696 vars, density 0.266, optimal yes | Optimal | 4.0 | Maximilian Schicker | 1 | Best submission | ||
| B4_4_9 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 48 vars, density 0.0854, optimal yes; QUBO, 696 vars, density 0.266, optimal yes | Optimal | 4.0 | Maximilian Schicker | 1 | Best submission | ||
| B5_25_1 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 240 vars, density 0.0274, optimal no; QUBO, 3480 vars, density 0.24, optimal no | Optimal | 14.0 | Maximilian Schicker | 1 | Best submission | ||
| B5_25_10 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 240 vars, density 0.0274, optimal no; QUBO, 3480 vars, density 0.24, optimal no | Best known | 14.0 | Maximilian Schicker | 1 | Best submission | ||
| B5_25_2 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 240 vars, density 0.0274, optimal no; QUBO, 3480 vars, density 0.24, optimal no | Best known | 14.0 | Maximilian Schicker | 1 | Best submission | ||
| B5_25_3 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 240 vars, density 0.0274, optimal no; QUBO, 3480 vars, density 0.24, optimal no | Optimal | 14.0 | Maximilian Schicker | 1 | Best submission | ||
| B5_25_4 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 240 vars, density 0.0274, optimal no; QUBO, 3480 vars, density 0.24, optimal no | Optimal | 13.0 | Maximilian Schicker | 1 | Best submission | ||
| B5_25_5 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 240 vars, density 0.0274, optimal no; QUBO, 3480 vars, density 0.24, optimal no | Best known | 14.0 | Maximilian Schicker | 1 | Best submission | ||
| B5_25_6 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 240 vars, density 0.0274, optimal no; QUBO, 3480 vars, density 0.24, optimal no | Best known | 14.0 | Maximilian Schicker | 1 | Best submission | ||
| B5_25_7 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 240 vars, density 0.0274, optimal no; QUBO, 3480 vars, density 0.24, optimal no | Optimal | 14.0 | Maximilian Schicker | 1 | Best submission | ||
| B5_25_8 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 240 vars, density 0.0274, optimal no; QUBO, 3480 vars, density 0.24, optimal no | Optimal | 14.0 | Maximilian Schicker | 1 | Best submission | ||
| B5_25_9 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 240 vars, density 0.0274, optimal no; QUBO, 3480 vars, density 0.24, optimal no | Optimal | 14.0 | Maximilian Schicker | 1 | Best submission | ||
| B5_5_1 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 240 vars, density 0.0274, optimal yes; QUBO, 3480 vars, density 0.24, optimal yes | Optimal | 5.0 | Maximilian Schicker | 1 | Best submission | ||
| B5_5_10 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 240 vars, density 0.0274, optimal yes; QUBO, 3480 vars, density 0.24, optimal yes | Optimal | 5.0 | Maximilian Schicker | 1 | Best submission | ||
| B5_5_2 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 240 vars, density 0.0274, optimal yes; QUBO, 3480 vars, density 0.24, optimal yes | Optimal | 5.0 | Maximilian Schicker | 1 | Best submission | ||
| B5_5_3 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 240 vars, density 0.0274, optimal yes; QUBO, 3480 vars, density 0.24, optimal yes | Optimal | 5.0 | Maximilian Schicker | 1 | Best submission | ||
| B5_5_4 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 240 vars, density 0.0274, optimal yes; QUBO, 3480 vars, density 0.24, optimal yes | Optimal | 5.0 | Maximilian Schicker | 1 | Best submission | ||
| B5_5_5 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 240 vars, density 0.0274, optimal yes; QUBO, 3480 vars, density 0.24, optimal yes | Optimal | 5.0 | Maximilian Schicker | 1 | Best submission | ||
| B5_5_6 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 240 vars, density 0.0274, optimal yes; QUBO, 3480 vars, density 0.24, optimal yes | Optimal | 5.0 | Maximilian Schicker | 1 | Best submission | ||
| B5_5_7 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 240 vars, density 0.0274, optimal yes; QUBO, 3480 vars, density 0.24, optimal yes | Optimal | 5.0 | Maximilian Schicker | 1 | Best submission | ||
| B5_5_8 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 240 vars, density 0.0274, optimal yes; QUBO, 3480 vars, density 0.24, optimal yes | Optimal | 5.0 | Maximilian Schicker | 1 | Best submission | ||
| B5_5_9 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 240 vars, density 0.0274, optimal yes; QUBO, 3480 vars, density 0.24, optimal yes | Optimal | 3.0 | Maximilian Schicker | 1 | Best submission | ||
| B6_36_1 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 1440 vars, density 0.00594, optimal no; QUBO, 20880 vars, density 0.234, optimal no | Best known | 22.0 | Maximilian Schicker | 1 | Best submission | ||
| B6_36_10 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 1440 vars, density 0.00594, optimal no; QUBO, 20880 vars, density 0.234, optimal no | Best known | 20.0 | Maximilian Schicker | 1 | Best submission | ||
| B6_36_2 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 1440 vars, density 0.00594, optimal no; QUBO, 20880 vars, density 0.234, optimal no | Best known | 22.0 | Maximilian Schicker | 1 | Best submission | ||
| B6_36_3 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 1440 vars, density 0.00594, optimal no; QUBO, 20880 vars, density 0.234, optimal no | Best known | 21.0 | Maximilian Schicker | 1 | Best submission | ||
| B6_36_4 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 1440 vars, density 0.00594, optimal no; QUBO, 20880 vars, density 0.234, optimal no | Best known | 22.0 | Maximilian Schicker | 1 | Best submission | ||
| B6_36_5 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 1440 vars, density 0.00594, optimal no; QUBO, 20880 vars, density 0.234, optimal no | Best known | 22.0 | Maximilian Schicker | 1 | Best submission | ||
| B6_36_6 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 1440 vars, density 0.00594, optimal no; QUBO, 20880 vars, density 0.234, optimal no | Best known | 22.0 | Maximilian Schicker | 1 | Best submission | ||
| B6_36_7 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 1440 vars, density 0.00594, optimal no; QUBO, 20880 vars, density 0.234, optimal no | Best known | 22.0 | Maximilian Schicker | 1 | Best submission | ||
| B6_36_8 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 1440 vars, density 0.00594, optimal no; QUBO, 20880 vars, density 0.234, optimal no | Best known | 22.0 | Maximilian Schicker | 1 | Best submission | ||
| B6_36_9 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 1440 vars, density 0.00594, optimal no; QUBO, 20880 vars, density 0.234, optimal no | Best known | 22.0 | Maximilian Schicker | 1 | Best submission | ||
| B6_6_1 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 1440 vars, density 0.00594, optimal yes; QUBO, 20880 vars, density 0.234, optimal yes | Optimal | 6.0 | Maximilian Schicker | 1 | Best submission | ||
| B6_6_10 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 1440 vars, density 0.00594, optimal yes; QUBO, 20880 vars, density 0.234, optimal yes | Optimal | 6.0 | Maximilian Schicker | 1 | Best submission | ||
| B6_6_2 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 1440 vars, density 0.00594, optimal yes; QUBO, 20880 vars, density 0.234, optimal yes | Optimal | 6.0 | Maximilian Schicker | 1 | Best submission | ||
| B6_6_3 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 1440 vars, density 0.00594, optimal yes; QUBO, 20880 vars, density 0.234, optimal yes | Optimal | 6.0 | Maximilian Schicker | 1 | Best submission | ||
| B6_6_4 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 1440 vars, density 0.00594, optimal yes; QUBO, 20880 vars, density 0.234, optimal yes | Optimal | 6.0 | Maximilian Schicker | 1 | Best submission | ||
| B6_6_5 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 1440 vars, density 0.00594, optimal yes; QUBO, 20880 vars, density 0.234, optimal yes | Optimal | 6.0 | Maximilian Schicker | 1 | Best submission | ||
| B6_6_6 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 1440 vars, density 0.00594, optimal yes; QUBO, 20880 vars, density 0.234, optimal yes | Optimal | 6.0 | Maximilian Schicker | 1 | Best submission | ||
| B6_6_7 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 1440 vars, density 0.00594, optimal yes; QUBO, 20880 vars, density 0.234, optimal yes | Optimal | 6.0 | Maximilian Schicker | 1 | Best submission | ||
| B6_6_8 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 1440 vars, density 0.00594, optimal yes; QUBO, 20880 vars, density 0.234, optimal yes | Optimal | 6.0 | Maximilian Schicker | 1 | Best submission | ||
| B6_6_9 | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices.Paper: MIP, 1440 vars, density 0.00594, optimal yes; QUBO, 20880 vars, density 0.234, optimal yes | Optimal | 6.0 | Maximilian Schicker | 1 | Best submission | ||
| qbench_03_dense | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance Solution | |||
| qbench_03_sparse | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance Solution | |||
| qbench_04_dense | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance Solution | |||
| qbench_04_sparse | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance Solution | |||
| qbench_05_dense | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance Solution | |||
| qbench_05_sparse | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance Solution | |||
| qbench_06_dense | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance Solution | |||
| qbench_06_sparse | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance Solution | |||
| qbench_07_dense | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance | |||
| qbench_07_sparse | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance | |||
| qbench_08_dense | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance | |||
| qbench_08_sparse | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance | |||
| qbench_09_dense | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance | |||
| qbench_09_sparse | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance | |||
| qbench_10_dense | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance | |||
| qbench_10_sparse | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance | |||
| qbench_11_dense | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance | |||
| qbench_11_sparse | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance | |||
| qbench_12_dense | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance | |||
| qbench_12_sparse | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance | |||
| qbench_13_dense | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance | |||
| qbench_13_sparse | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance | |||
| qbench_14_dense | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance | |||
| qbench_14_sparse | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance | |||
| qbench_15_dense | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance | |||
| qbench_15_sparse | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance | |||
| qbench_16_dense | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance | |||
| qbench_16_sparse | Minimum Birkhoff Decomposition | A decomposition benchmark that tests whether a method can find sparse convex combinations of permutation matrices. | .json | Open | 0 | Instance | |||
| stp_s003_l1_t2_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Optimal | 0 | Instance Solution | |||
| stp_s003_l1_t2_h3_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Optimal | 0 | Instance Solution | |||
| stp_s003_l1_t2_h5_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Optimal | 0 | Instance Solution | |||
| stp_s003_l1_t3_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Optimal | 0 | Instance Solution | |||
| stp_s003_l1_t3_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Optimal | 0 | Instance Solution | |||
| stp_s003_l2_t2_h4_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Optimal | 0 | Instance Solution | |||
| stp_s003_l2_t2_h5_rs37235 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Optimal | 0 | Instance Solution | |||
| stp_s004_l1_t2_h4_rs37235 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Optimal | 0 | Instance Solution | |||
| stp_s004_l1_t3_h3_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Optimal | 0 | Instance Solution | |||
| stp_s004_l1_t3_h3_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Optimal | 0 | Instance Solution | |||
| stp_s020_l2_t3_h2_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 78372 vars, density 6.37e-05, optimal yes; QUBO, 127676 vars, density 0.000162, optimal yes | directory | Optimal | 0 | Instance Solution | |||
| stp_s020_l2_t3_h3_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 95160 vars, density 5.19e-05, optimal yes; QUBO, 154510 vars, density 0.000148, optimal yes | directory | Best known | 0 | Instance Solution | |||
| stp_s020_l2_t4_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 99840 vars, density 5.84e-05, optimal yes; QUBO, 158232 vars, density 0.000133, optimal yes | directory | Best known | 0 | Instance Solution | |||
| stp_s020_l2_t4_h3_rs37235 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 81664 vars, density 6.3e-05, optimal yes; QUBO, 123288 vars, density 0.000177, optimal yes | directory | Optimal | 0 | Instance Solution | |||
| stp_s020_l3_t3_h2_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 211820 vars, density 2.21e-05, optimal yes; QUBO, n/a vars, optimal yes | directory | Optimal | 0 | Instance Solution | |||
| stp_s020_l3_t4_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 160160 vars, density 3.44e-05, optimal yes; QUBO, n/a vars, optimal yes | directory | Best known | 0 | Instance Solution | |||
| stp_s020_l3_t4_h2_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 132880 vars, density 3.97e-05, optimal yes; QUBO, n/a vars, optimal yes | directory | Optimal | 0 | Instance Solution | |||
| stp_s020_l3_t4_h3_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 137540 vars, density 3.63e-05, optimal yes; QUBO, n/a vars, optimal yes | directory | Optimal | 0 | Instance Solution | |||
| stp_s020_l4_t3_h3_rs37235 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Best known | 0 | Instance Solution | |||
| stp_s020_l4_t4_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 245920 vars, density 2.44e-05, optimal yes; QUBO, n/a vars, optimal yes | directory | Best known | 0 | Instance Solution | |||
| stp_s020_l4_t4_h3_rs37235 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 179872 vars, density 2.95e-05, optimal yes; QUBO, n/a vars, optimal yes | directory | Optimal | 0 | Instance Solution | |||
| stp_s020_l5_t3_h3_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 317880 vars, density 1.68e-05, optimal yes; QUBO, n/a vars, optimal yes | directory | Best known | 0 | Instance Solution | |||
| stp_s020_l5_t4_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 313200 vars, density 1.93e-05, optimal yes; QUBO, n/a vars, optimal yes | directory | Best known | 0 | Instance Solution | |||
| stp_s020_l5_t4_h3_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Optimal | 0 | Instance Solution | |||
| stp_s030_l2_t3_h1_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 267654 vars, density 1.87e-05, optimal yes; QUBO, n/a vars, optimal yes | directory | Optimal | 0 | Instance Solution | |||
| stp_s030_l2_t3_h3_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 269696 vars, density 1.89e-05, optimal yes; QUBO, n/a vars, optimal yes | directory | Optimal | 0 | Instance Solution | |||
| stp_s030_l2_t4_h2_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 255240 vars, density 2.04e-05, optimal yes; QUBO, n/a vars, optimal yes | directory | Optimal | 0 | Instance Solution | |||
| stp_s030_l2_t5_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 276288 vars, density 2.1e-05, optimal yes; QUBO, n/a vars, optimal yes | directory | Optimal | 0 | Instance Solution | |||
| stp_s030_l3_t3_h1_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 361764 vars, density 1.41e-05, optimal yes; QUBO, n/a vars, optimal yes | directory | Optimal | 0 | Instance Solution | |||
| stp_s030_l3_t3_h2_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 358488 vars, density 1.32e-05, optimal yes; QUBO, n/a vars, optimal yes | directory | Optimal | 0 | Instance Solution | |||
| stp_s030_l3_t4_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Best known | 0 | Instance Solution | |||
| stp_s030_l3_t5_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 473076 vars, density 1.23e-05, optimal yes; QUBO, n/a vars, optimal yes | directory | Optimal | 0 | Instance Solution | |||
| stp_s030_l4_t3_h1_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 499044 vars, density 1.03e-05, optimal yes; QUBO, n/a vars, optimal yes | directory | Optimal | 0 | Instance Solution | |||
| stp_s030_l4_t4_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 676200 vars, density 8.87e-06, optimal yes; QUBO, n/a vars, optimal yes | directory | Best known | 0 | Instance Solution | |||
| stp_s030_l4_t4_h2_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 592410 vars, density 9.9e-06, optimal yes; QUBO, n/a vars, optimal yes | directory | Optimal | 0 | Instance Solution | |||
| stp_s030_l5_t3_h1_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 636324 vars, density 8.14e-06, optimal yes; QUBO, n/a vars, optimal yes | directory | Optimal | 0 | Instance Solution | |||
| stp_s030_l5_t4_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry.Paper: MIP, 762600 vars, density 7.76e-06, optimal yes; QUBO, n/a vars, optimal yes | directory | Optimal | 0 | Instance Solution | |||
| stp_s040_l2_t3_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s040_l2_t3_h2_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s040_l2_t4_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s040_l2_t4_h3_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s040_l3_t3_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s040_l3_t4_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s040_l3_t4_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s040_l4_t3_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s040_l4_t4_h0_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s040_l4_t4_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s040_l4_t4_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s040_l4_t5_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s040_l5_t3_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s040_l5_t4_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s040_l5_t4_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s040_l5_t5_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s050_l2_t3_h1_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Best known | 0 | Instance Solution | |||
| stp_s050_l2_t3_h3_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s050_l2_t4_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s050_l2_t4_h1_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Best known | 0 | Instance Solution | |||
| stp_s050_l2_t4_h3_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s050_l2_t5_h1_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s050_l3_t3_h3_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s050_l3_t4_h0_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s050_l3_t4_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s050_l3_t4_h2_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s050_l3_t4_h2_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s050_l3_t5_h1_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s050_l3_t6_h2_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Best known | 0 | Instance Solution | |||
| stp_s050_l4_t4_h0_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s050_l4_t4_h2_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s050_l4_t4_h2_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s050_l4_t5_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s050_l5_t4_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s050_l5_t4_h2_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l2_t4_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l2_t4_h1_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l2_t4_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l2_t5_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l3_t3_h2_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l3_t4_h0_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l3_t4_h1_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Best known | 0 | Instance Solution | |||
| stp_s060_l3_t4_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l3_t4_h2_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l3_t4_h2_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l3_t5_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l3_t5_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l3_t5_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l3_t5_h2_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l3_t5_h2_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l3_t6_h2_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l4_t3_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l4_t3_h1_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l4_t3_h2_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l4_t4_h0_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l4_t4_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l4_t4_h1_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l4_t4_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l4_t5_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l5_t3_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Optimal | 0 | Instance Solution | |||
| stp_s060_l5_t3_h1_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l5_t4_h0_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l5_t4_h1_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l5_t4_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l5_t5_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l5_t5_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s060_l5_t6_h2_rs37235 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s070_l2_t3_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s070_l2_t4_h0_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s070_l2_t6_h1_rs37235 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s070_l3_t4_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s070_l3_t4_h2_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s070_l3_t4_h2_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s070_l3_t5_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s070_l3_t5_h2_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s070_l4_t3_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s070_l4_t4_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s070_l4_t4_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s070_l4_t4_h1_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s070_l4_t4_h2_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s070_l4_t6_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Best known | 0 | Instance Solution | |||
| stp_s070_l5_t3_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s070_l5_t4_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s070_l5_t4_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s070_l5_t4_h2_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s070_l5_t5_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s070_l5_t5_h1_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s080_l2_t4_h0_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s080_l2_t4_h1_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s080_l2_t4_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s080_l2_t5_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s080_l3_t3_h1_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s080_l3_t4_h0_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s080_l3_t5_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s080_l3_t5_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s080_l4_t3_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s080_l4_t3_h1_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s080_l4_t4_h0_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s080_l4_t4_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s080_l4_t4_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s080_l4_t5_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s080_l4_t5_h1_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s080_l5_t4_h0_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s080_l5_t4_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l2_t3_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l2_t3_h1_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l2_t3_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l2_t3_h1_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l2_t3_h2_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l2_t3_h2_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l2_t5_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l3_t3_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l3_t3_h1_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l3_t3_h2_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l3_t4_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l3_t5_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l3_t5_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l3_t5_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l3_t6_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l4_t3_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l4_t3_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l4_t3_h1_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l4_t3_h2_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l4_t4_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l4_t4_h1_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l4_t5_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l5_t3_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l5_t4_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l5_t4_h1_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s090_l5_t6_h0_rs37235 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l2_t3_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l2_t3_h2_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l2_t3_h3_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l2_t4_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l2_t4_h3_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l2_t5_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l3_t3_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l3_t3_h2_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l3_t3_h3_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l3_t4_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l3_t4_h3_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l4_t3_h0_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l4_t3_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l4_t3_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l4_t3_h2_rs123 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l4_t3_h2_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l4_t4_h3_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l4_t6_h2_rs37235 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l5_t3_h0_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l5_t3_h1_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l5_t3_h2_rs24098 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l5_t3_h2_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| stp_s100_l5_t4_h2_rs97531 | Steiner Tree Packing | A routing benchmark for packing disjoint connection structures through constrained grid geometry. | directory | Open | 0 | Instance | |||
| Addition_000_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00837, optimal yes; QUBO, 4667 vars, density 0.0723, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_000_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 15920 vars, density 0.00278, optimal no; QUBO, 19099 vars, density 0.0291, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_000_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.027, optimal yes; QUBO, 1737 vars, density 0.123, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_000_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0535, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_003_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00878, optimal yes; QUBO, 5118 vars, density 0.0603, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_003_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00468, optimal no; QUBO, 11077 vars, density 0.0363, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_003_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0265, optimal yes; QUBO, 1880 vars, density 0.108, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_003_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0511, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_005_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.009, optimal yes; QUBO, 5604 vars, density 0.0557, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_005_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00376, optimal no; QUBO, 16481 vars, density 0.0275, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_005_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.026, optimal yes; QUBO, 2074 vars, density 0.0915, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_005_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0569, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_007_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00881, optimal yes; QUBO, 5666 vars, density 0.0549, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_007_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00474, optimal no; QUBO, 12049 vars, density 0.033, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_007_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0226, optimal yes; QUBO, 2098 vars, density 0.0907, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_007_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0446, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_015_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00873, optimal yes; QUBO, 5605 vars, density 0.0549, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_015_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00474, optimal no; QUBO, 11995 vars, density 0.0322, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_015_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0226, optimal yes; QUBO, 2082 vars, density 0.0846, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_015_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.048, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_016_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00894, optimal yes; QUBO, 5586 vars, density 0.0552, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_016_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 15920 vars, density 0.00303, optimal no; QUBO, 21961 vars, density 0.0242, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_016_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0242, optimal yes; QUBO, 2063 vars, density 0.0892, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_016_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0466, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_018_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00883, optimal yes; QUBO, 5655 vars, density 0.0563, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_018_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 15920 vars, density 0.00301, optimal no; QUBO, 22022 vars, density 0.0244, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_018_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0246, optimal yes; QUBO, 2142 vars, density 0.0907, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_018_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.044, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_020_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.009, optimal yes; QUBO, 5682 vars, density 0.0573, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_020_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00471, optimal no; QUBO, 12036 vars, density 0.0324, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_020_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0238, optimal yes; QUBO, 2118 vars, density 0.091, optimal no | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_020_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0456, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_024_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00823, optimal no; QUBO, 4648 vars, density 0.0663, optimal no | Medium | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_024_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00343, optimal no; QUBO, 14196 vars, density 0.0333, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_024_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0246, optimal yes; QUBO, 1695 vars, density 0.124, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_024_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0533, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_025_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00847, optimal no; QUBO, 4622 vars, density 0.0705, optimal no | Medium | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_025_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00452, optimal no; QUBO, 10186 vars, density 0.0417, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_025_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0248, optimal yes; QUBO, 1660 vars, density 0.13, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_025_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0534, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_027_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00871, optimal no; QUBO, 5115 vars, density 0.0598, optimal no | Medium | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_027_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00363, optimal no; QUBO, 15321 vars, density 0.0301, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_027_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0256, optimal yes; QUBO, 1882 vars, density 0.106, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_027_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0528, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_028_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00872, optimal yes; QUBO, 5631 vars, density 0.0547, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_028_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 15920 vars, density 0.00301, optimal no; QUBO, 22020 vars, density 0.0243, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_028_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0235, optimal yes; QUBO, 2086 vars, density 0.0896, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_028_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0503, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_029_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00858, optimal yes; QUBO, 5597 vars, density 0.0517, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_029_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 15920 vars, density 0.00301, optimal no; QUBO, 21999 vars, density 0.0242, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_029_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0223, optimal yes; QUBO, 2058 vars, density 0.0908, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_029_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0497, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_030_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00898, optimal yes; QUBO, 5645 vars, density 0.0592, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_030_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00371, optimal no; QUBO, 16503 vars, density 0.0275, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_030_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0239, optimal yes; QUBO, 2101 vars, density 0.0913, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_030_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0431, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_032_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00859, optimal yes; QUBO, 5643 vars, density 0.0519, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_032_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00475, optimal no; QUBO, 12038 vars, density 0.0335, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_032_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0228, optimal yes; QUBO, 2108 vars, density 0.0894, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_032_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0484, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_034_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00869, optimal yes; QUBO, 5651 vars, density 0.0545, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_034_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00371, optimal no; QUBO, 16524 vars, density 0.0275, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_034_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0244, optimal yes; QUBO, 2134 vars, density 0.0898, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_034_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0471, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_035_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.0086, optimal yes; QUBO, 5650 vars, density 0.0519, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_035_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00475, optimal no; QUBO, 12032 vars, density 0.0334, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_035_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0227, optimal yes; QUBO, 2104 vars, density 0.0881, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_035_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0477, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_043_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00868, optimal yes; QUBO, 5592 vars, density 0.0514, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_043_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00475, optimal no; QUBO, 11990 vars, density 0.0324, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_043_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0227, optimal yes; QUBO, 2047 vars, density 0.0888, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_043_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0522, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_044_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00845, optimal yes; QUBO, 5651 vars, density 0.0496, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_044_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00472, optimal no; QUBO, 12032 vars, density 0.033, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_044_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0227, optimal yes; QUBO, 2101 vars, density 0.0879, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_044_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0493, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_046_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00887, optimal yes; QUBO, 5572 vars, density 0.053, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_046_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00479, optimal no; QUBO, 11955 vars, density 0.0322, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_046_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0242, optimal yes; QUBO, 2025 vars, density 0.0901, feasible yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_046_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0515, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_047_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00889, optimal yes; QUBO, 5560 vars, density 0.051, optimal yes | Medium | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_047_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00379, optimal no; QUBO, 16449 vars, density 0.0275, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_047_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.025, optimal yes; QUBO, 2025 vars, density 0.0945, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_047_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0529, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_048_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00872, optimal no; QUBO, 5661 vars, density 0.0531, optimal no | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_048_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.0047, optimal no; QUBO, 12015 vars, density 0.0327, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_048_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0244, optimal yes; QUBO, 2123 vars, density 0.0916, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_048_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0459, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_050_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00884, optimal yes; QUBO, 5655 vars, density 0.0563, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_050_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 15920 vars, density 0.00301, optimal no; QUBO, 22012 vars, density 0.0244, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_050_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.023, optimal yes; QUBO, 2105 vars, density 0.0889, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_050_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0492, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_052_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00884, optimal yes; QUBO, 5656 vars, density 0.0556, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_052_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.0047, optimal no; QUBO, 12020 vars, density 0.0326, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_052_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0234, optimal yes; QUBO, 2115 vars, density 0.0917, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_052_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0485, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_064_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00883, optimal yes; QUBO, 5564 vars, density 0.0503, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_064_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00378, optimal no; QUBO, 16449 vars, density 0.0277, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_064_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0271, optimal yes; QUBO, 2055 vars, density 0.0932, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_064_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.053, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_066_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.0086, optimal yes; QUBO, 5691 vars, density 0.0526, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_066_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00368, optimal no; QUBO, 16529 vars, density 0.0276, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_066_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0221, optimal yes; QUBO, 2119 vars, density 0.0876, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_066_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0458, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_067_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00863, optimal yes; QUBO, 5607 vars, density 0.0493, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_067_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00376, optimal no; QUBO, 16500 vars, density 0.0279, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_067_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.024, optimal yes; QUBO, 2066 vars, density 0.0933, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_067_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0509, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_068_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.0088, optimal yes; QUBO, 5657 vars, density 0.0554, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_068_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00374, optimal no; QUBO, 16556 vars, density 0.0281, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_068_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0226, optimal yes; QUBO, 2095 vars, density 0.0899, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_068_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.049, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_069_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.0087, optimal yes; QUBO, 5646 vars, density 0.0537, feasible yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_069_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00469, optimal no; QUBO, 12015 vars, density 0.0321, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_069_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0238, optimal yes; QUBO, 2120 vars, density 0.0904, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_069_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0467, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_070_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00874, optimal yes; QUBO, 5664 vars, density 0.0531, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_070_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00469, optimal no; QUBO, 12034 vars, density 0.0317, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_070_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0232, optimal yes; QUBO, 2108 vars, density 0.0881, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_070_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0465, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_082_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00901, optimal yes; QUBO, 5551 vars, density 0.0517, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_082_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00381, optimal no; QUBO, 16434 vars, density 0.0278, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_082_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0279, optimal yes; QUBO, 2036 vars, density 0.0943, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_082_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0574, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_083_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00871, optimal yes; QUBO, 5587 vars, density 0.0506, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_083_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00375, optimal no; QUBO, 16477 vars, density 0.0272, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_083_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0233, optimal yes; QUBO, 2038 vars, density 0.0902, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_083_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0459, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_085_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00885, optimal yes; QUBO, 5591 vars, density 0.0522, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_085_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00482, optimal no; QUBO, 11977 vars, density 0.0329, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_085_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0254, optimal yes; QUBO, 2068 vars, density 0.0934, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_085_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0495, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_090_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00893, optimal yes; QUBO, 5605 vars, density 0.0561, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_090_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 15920 vars, density 0.00304, optimal no; QUBO, 21978 vars, density 0.0246, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_090_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0237, optimal yes; QUBO, 2061 vars, density 0.0931, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_090_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0483, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_100_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00858, optimal yes; QUBO, 5113 vars, density 0.058, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_100_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00363, optimal no; QUBO, 15351 vars, density 0.03, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_100_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0239, optimal yes; QUBO, 1894 vars, density 0.103, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_100_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0539, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_104_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00876, optimal yes; QUBO, 5168 vars, density 0.0632, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_104_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00461, optimal no; QUBO, 11134 vars, density 0.0361, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_104_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.025, optimal yes; QUBO, 1926 vars, density 0.105, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_104_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0493, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_105_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00846, optimal no; QUBO, 5560 vars, density 0.0489, optimal no | Medium | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_105_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00468, optimal no; QUBO, 11950 vars, density 0.0312, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_105_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0207, optimal yes; QUBO, 1992 vars, density 0.0899, feasible yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_105_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0396, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_107_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00843, optimal yes; QUBO, 5572 vars, density 0.0485, feasible yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_107_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00472, optimal no; QUBO, 11984 vars, density 0.032, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_107_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0227, optimal yes; QUBO, 2036 vars, density 0.0897, feasible yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_107_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0414, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_109_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00875, optimal yes; QUBO, 5592 vars, density 0.0531, feasible yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_109_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00476, optimal no; QUBO, 11981 vars, density 0.0323, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_109_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0234, optimal yes; QUBO, 2048 vars, density 0.0934, feasible yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_109_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.046, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_110_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00897, optimal yes; QUBO, 5604 vars, density 0.0568, feasible yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_110_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.0048, optimal no; QUBO, 11992 vars, density 0.0333, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_110_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0239, optimal yes; QUBO, 2057 vars, density 0.0933, feasible yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_110_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0495, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_111_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00866, optimal yes; QUBO, 5658 vars, density 0.0521, feasible yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_111_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 15920 vars, density 0.00302, optimal no; QUBO, 22037 vars, density 0.0245, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_111_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.023, optimal yes; QUBO, 2104 vars, density 0.0914, feasible yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_111_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0471, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_128_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00865, optimal no; QUBO, 5655 vars, density 0.0515, optimal no | Medium | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_128_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00473, optimal no; QUBO, 12024 vars, density 0.0325, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_128_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0246, optimal yes; QUBO, 2123 vars, density 0.0903, feasible yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_128_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0446, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_129_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00873, optimal yes; QUBO, 5652 vars, density 0.0535, feasible yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_129_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00473, optimal no; QUBO, 12032 vars, density 0.0327, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_129_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0243, optimal yes; QUBO, 2128 vars, density 0.0887, feasible yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_129_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0507, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_130_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00905, optimal yes; QUBO, 5612 vars, density 0.0578, feasible yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_130_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 15920 vars, density 0.00303, optimal no; QUBO, 21969 vars, density 0.0245, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_130_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0231, optimal yes; QUBO, 2044 vars, density 0.0915, feasible yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_130_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0451, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_132_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00878, optimal yes; QUBO, 5659 vars, density 0.0557, feasible yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_132_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.0047, optimal no; QUBO, 12035 vars, density 0.032, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_132_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0242, optimal yes; QUBO, 2130 vars, density 0.0902, feasible yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_132_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0509, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_133_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.0088, optimal yes; QUBO, 5661 vars, density 0.0541, feasible yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_133_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00373, optimal no; QUBO, 16536 vars, density 0.0278, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_133_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0236, optimal yes; QUBO, 2117 vars, density 0.0904, feasible yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_133_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.0445, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_152_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00898, optimal yes; QUBO, 5613 vars, density 0.0572, feasible yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_152_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00482, optimal no; QUBO, 12000 vars, density 0.0333, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Addition_152_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0241, optimal yes; QUBO, 2062 vars, density 0.0944, feasible yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Addition_152_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 408 vars, density 0.046, optimal yes; QUBO, n/a vars, optimal n/a | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Early_005_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00861, optimal yes; QUBO, 5624 vars, density 0.0516, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Early_005_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00375, optimal no; QUBO, 16509 vars, density 0.028, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Early_005_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.023, optimal yes; QUBO, 2108 vars, density 0.0882, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Early_005_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge. | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Early_010_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00867, optimal yes; QUBO, 5648 vars, density 0.0535, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Early_010_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 15920 vars, density 0.00302, optimal no; QUBO, 22030 vars, density 0.0247, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Early_010_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.024, optimal yes; QUBO, 2122 vars, density 0.0922, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Early_010_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge. | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| ITC2021_Early_01 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8608 vars, density 0.00862, optimal no; QUBO, 11791 vars, density 0.0508, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Early_02 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8608 vars, density 0.00779, optimal no; QUBO, 13577 vars, density 0.0403, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Early_03 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8608 vars, density 0.00837, optimal no; QUBO, 11869 vars, density 0.0444, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Early_04 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00338, optimal no; QUBO, 14206 vars, density 0.0322, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Early_05 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00375, optimal no; QUBO, 16509 vars, density 0.028, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Early_06 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 12204 vars, density 0.00656, optimal no; QUBO, 19070 vars, density 0.0348, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Early_07 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00368, optimal no; QUBO, 15340 vars, density 0.0297, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Early_08 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 12204 vars, density 0.00775, optimal no; QUBO, 16665 vars, density 0.0411, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Early_09 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 12204 vars, density 0.00771, optimal no; QUBO, 16708 vars, density 0.0411, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Early_10 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 15920 vars, density 0.00302, optimal no; QUBO, 22030 vars, density 0.0247, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Early_11 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 15920 vars, density 0.00304, optimal no; QUBO, 22025 vars, density 0.0241, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Early_12 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 15920 vars, density 0.00296, optimal no; QUBO, 21936 vars, density 0.0191, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Early_13 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 15920 vars, density 0.00289, optimal no; QUBO, 20392 vars, density 0.0249, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Early_14 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 16680 vars, density 0.00685, optimal no; QUBO, 22361 vars, density 0.0388, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Early_15 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 16680 vars, density 0.00582, optimal no; QUBO, 25364 vars, density 0.0325, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Late_01 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8608 vars, density 0.00812, optimal no; QUBO, 13092 vars, density 0.04, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Late_02 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00474, optimal no; QUBO, 12043 vars, density 0.0317, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Late_03 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8608 vars, density 0.00817, optimal no; QUBO, 12924 vars, density 0.0384, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Late_04 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00324, optimal no; QUBO, 14099 vars, density 0.0233, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Late_05 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 12204 vars, density 0.00657, optimal no; QUBO, 19095 vars, density 0.0386, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Late_06 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00339, optimal no; QUBO, 14197 vars, density 0.0259, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Late_07 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00352, optimal no; QUBO, 15228 vars, density 0.0275, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Late_08 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00323, optimal no; QUBO, 14073 vars, density 0.0231, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Late_09 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 12204 vars, density 0.00669, optimal no; QUBO, 19000 vars, density 0.0333, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Late_10 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 15920 vars, density 0.00298, optimal no; QUBO, 22035 vars, density 0.0241, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Late_11 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 16680 vars, density 0.00666, optimal no; QUBO, 22208 vars, density 0.0437, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Late_12 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 15920 vars, density 0.00303, optimal no; QUBO, 22006 vars, density 0.0239, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Late_13 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 16680 vars, density 0.00584, optimal no; QUBO, 25362 vars, density 0.0325, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Late_14 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 16680 vars, density 0.00582, optimal no; QUBO, 25238 vars, density 0.0319, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Late_15 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 16680 vars, density 0.00686, optimal no; QUBO, 22356 vars, density 0.0389, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Middle_01 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00449, optimal no; QUBO, 10181 vars, density 0.0414, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Middle_02 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00471, optimal no; QUBO, 12035 vars, density 0.0326, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Middle_03 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8608 vars, density 0.00778, optimal no; QUBO, 14000 vars, density 0.0375, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Middle_04 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00346, optimal no; QUBO, 15196 vars, density 0.0216, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Middle_05 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 12204 vars, density 0.00732, optimal no; QUBO, 16688 vars, density 0.0412, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Middle_06 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00364, optimal no; QUBO, 16414 vars, density 0.0211, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Middle_07 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00361, optimal no; QUBO, 15344 vars, density 0.0236, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Middle_08 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00367, optimal no; QUBO, 16341 vars, density 0.02, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Middle_09 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 12204 vars, density 0.00714, optimal no; QUBO, 17849 vars, density 0.0365, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Middle_10 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 15920 vars, density 0.0027, optimal no; QUBO, 19057 vars, density 0.0279, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Middle_11 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 16680 vars, density 0.00569, optimal no; QUBO, 25240 vars, density 0.0325, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Middle_12 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 16680 vars, density 0.0061, optimal no; QUBO, 23649 vars, density 0.035, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Middle_13 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 15920 vars, density 0.00293, optimal no; QUBO, 20562 vars, density 0.0212, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Middle_14 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 16680 vars, density 0.00587, optimal no; QUBO, 25239 vars, density 0.0353, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| ITC2021_Middle_15 | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 15920 vars, density 0.00265, optimal no; QUBO, 18985 vars, density 0.0206, optimal no | ITC2021 | .xml.gz | Open | 0 | Instance Solution | ||
| Late_005_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00879, optimal yes; QUBO, 5590 vars, density 0.0542, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Late_005_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 11592 vars, density 0.00374, optimal no; QUBO, 16503 vars, density 0.0278, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Late_005_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.024, optimal yes; QUBO, 2065 vars, density 0.0916, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Late_005_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge. | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| Middle_002_Medium | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 3408 vars, density 0.00875, optimal yes; QUBO, 5654 vars, density 0.0544, optimal yes | Medium | .xml.gz | Optimal | 0 | Instance Solution | ||
| Middle_002_NoSoft | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 8128 vars, density 0.00471, optimal no; QUBO, 12035 vars, density 0.0326, optimal no | Large | .xml.gz | Open | 0 | Instance Solution | ||
| Middle_002_Small | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge.Paper: MIP, 992 vars, density 0.0233, optimal yes; QUBO, 2110 vars, density 0.0903, optimal yes | Small | .xml.gz | Optimal | 0 | Instance Solution | ||
| Middle_002_Tiny | Sports Tournament Scheduling | A real-world-like scheduling benchmark where feasibility under many interacting constraints is the central challenge. | Tiny | .xml.gz | Optimal | 0 | Instance Solution | ||
| po_a010_t10_orig | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Best known | -0.030604 | Daniel Hinderink (hiq-lab) | 1 | Instance Solution Best submission | |
| po_a010_t10_s00 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance Solution | |||
| po_a010_t10_s01 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance Solution | |||
| po_a010_t10_s02 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance Solution | |||
| po_a010_t15_orig | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Best known | -0.007064 | Daniel Hinderink (hiq-lab) | 1 | Instance Solution Best submission | |
| po_a010_t15_s00 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance Solution | |||
| po_a010_t15_s01 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance Solution | |||
| po_a010_t15_s02 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance Solution | |||
| po_a050_t10_orig | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance Solution | |||
| po_a050_t10_s00 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance Solution | |||
| po_a050_t10_s01 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance Solution | |||
| po_a050_t10_s02 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance Solution | |||
| po_a050_t15_orig | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance Solution | |||
| po_a050_t15_s00 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance Solution | |||
| po_a050_t15_s01 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance Solution | |||
| po_a050_t15_s02 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance Solution | |||
| po_a200_t10_orig | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance | |||
| po_a200_t10_s00 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance | |||
| po_a200_t10_s01 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance | |||
| po_a200_t10_s02 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance | |||
| po_a200_t15_orig | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance | |||
| po_a200_t15_s00 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance | |||
| po_a200_t15_s01 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance | |||
| po_a200_t15_s02 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance | |||
| po_a400_t10_orig | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance | |||
| po_a400_t10_s00 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance | |||
| po_a400_t10_s01 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance | |||
| po_a400_t10_s02 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance | |||
| po_a400_t15_orig | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance | |||
| po_a400_t15_s00 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance | |||
| po_a400_t15_s01 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance | |||
| po_a400_t15_s02 | Portfolio Optimization | A finance benchmark for balancing risk, returns, transaction costs, and time-linked binary decisions. | directory | Open | 0 | Instance | |||
| C125-9 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 125 vars, density 0.016, optimal yes; QUBO, 125 vars, density 0.116, optimal yes | .gph | Optimal | 34.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| C4000-5 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 4000 vars, density 0.0005, optimal no; QUBO, 4000 vars, density 0.5, optimal no | .gph | Best known | 18 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| C500-9 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 500 vars, density 0.004, optimal no; QUBO, 500 vars, density 0.103, optimal no | .gph | Best known | 57 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| MANN-a9 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 45 vars, density 0.0444, optimal yes; QUBO, 45 vars, density 0.93, optimal yes | .gph | Optimal | 3.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| R_1000_005_1 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 1000 vars, density 0.002, optimal no; QUBO, 1000 vars, density 0.0513, optimal no | .gph | Best known | 115 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| R_500_005_1 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 500 vars, density 0.004, optimal no; QUBO, 500 vars, density 0.0539, optimal no | .gph | Best known | 90 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| aves-sparrow-social | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 52 vars, density 0.0385, optimal yes; QUBO, 52 vars, density 0.367, optimal yes | .gph | Optimal | 13.0 | Maximilian Schicker | 4 | Instance Solution Best submission | |
| brock200-1 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 200 vars, density 0.01, optimal yes; QUBO, 200 vars, density 0.748, optimal yes | .gph | Optimal | 6.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| brock200-2 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 200 vars, density 0.01, optimal yes; QUBO, 200 vars, density 0.509, optimal yes | .gph | Optimal | 12.0 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| brock200-3 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 200 vars, density 0.01, optimal yes; QUBO, 200 vars, density 0.609, optimal yes | .gph | Optimal | 9.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| brock200-4 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 200 vars, density 0.01, optimal yes; QUBO, 200 vars, density 0.661, optimal yes | .gph | Optimal | 8.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| brock400-1 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 400 vars, density 0.005, optimal yes; QUBO, 400 vars, density 0.255, optimal yes | .gph | Optimal | 27.0 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| brock800-1 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 800 vars, density 0.0025, optimal yes; QUBO, 800 vars, density 0.352, optimal yes | .gph | Optimal | 23 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| c-fat200-1 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 200 vars, density 0.01, optimal yes; QUBO, 200 vars, density 0.0863, optimal yes | .gph | Optimal | 18.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| chesapeake | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 39 vars, density 0.0513, optimal yes; QUBO, 39 vars, density 0.268, optimal yes | .gph | Optimal | 17.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| es60fst01 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 123 vars, density 0.0163, optimal yes; QUBO, 123 vars, density 0.037, optimal yes | .gph | Optimal | 60.0 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| es60fst02 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 186 vars, density 0.0108, optimal yes; QUBO, 186 vars, density 0.0268, optimal yes | .gph | Optimal | 88.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| es60fst03 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 113 vars, density 0.0177, optimal yes; QUBO, 113 vars, density 0.0396, optimal yes | .gph | Optimal | 55.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| es60fst04 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 162 vars, density 0.0123, optimal yes; QUBO, 162 vars, density 0.0303, optimal yes | .gph | Optimal | 78.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| farm | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 17 vars, density 0.118, optimal yes; QUBO, 17 vars, density 0.366, optimal yes | .gph | Optimal | 10.0 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| football | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 35 vars, density 0.0571, optimal yes; QUBO, 35 vars, density 0.243, optimal yes | .gph | Optimal | 16.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| frb100-40 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 4000 vars, density 0.0005, optimal no; QUBO, 4000 vars, density 0.0721, optimal no | .gph | Best known | 93 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| frb45-21-3 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 945 vars, density 0.00212, optimal yes; QUBO, 945 vars, density 0.132, optimal yes | .gph | Optimal | 44.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| frb50-23-3 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 1150 vars, density 0.00174, optimal yes; QUBO, 1150 vars, density 0.124, optimal yes | .gph | Optimal | 49 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| frb53-24-1 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 1272 vars, density 0.00157, optimal no; QUBO, 1272 vars, density 0.118, optimal no | .gph | Best known | 51 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| frb59-26-2 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 1534 vars, density 0.0013, optimal no; QUBO, 1534 vars, density 0.108, optimal no | .gph | Best known | 56.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| gen200_p0-9_44 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 200 vars, density 0.01, optimal yes; QUBO, 200 vars, density 0.109, optimal yes | .gph | Optimal | 44.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| hamming10-4 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 1024 vars, density 0.00195, optimal no; QUBO, 1024 vars, density 0.173, optimal no | .gph | Best known | 40.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| hamming6-2 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 64 vars, density 0.0312, optimal yes; QUBO, 64 vars, density 0.908, optimal yes | .gph | Optimal | 2.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| hamming6-4 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 64 vars, density 0.0312, optimal yes; QUBO, 64 vars, density 0.369, optimal yes | .gph | Optimal | 12.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| ibm32 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 32 vars, density 0.0625, optimal yes; QUBO, 32 vars, density 0.231, optimal yes | .gph | Optimal | 13.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| insecta-ant-colony1-day38 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 56 vars, density 0.0357, optimal yes; QUBO, 56 vars, density 0.746, optimal yes | .gph | Optimal | 6.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| insecta-ant-colony3-day09 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 160 vars, density 0.0125, optimal yes; QUBO, 160 vars, density 0.702, optimal yes | .gph | Optimal | 9.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| johnson16-2-4 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 120 vars, density 0.0167, optimal yes; QUBO, 120 vars, density 0.769, optimal yes | .gph | Optimal | 15.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| johnson8-2-4 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 28 vars, density 0.0714, optimal yes; QUBO, 28 vars, density 0.586, optimal yes | .gph | Optimal | 7.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| johnson8-4-4 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 70 vars, density 0.0286, optimal yes; QUBO, 70 vars, density 0.775, optimal yes | .gph | Optimal | 5.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| karate | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 34 vars, density 0.0588, optimal yes; QUBO, 34 vars, density 0.188, optimal yes | .gph | Optimal | 20.0 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| keller4 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 171 vars, density 0.0117, optimal yes; QUBO, 171 vars, density 0.358, optimal yes | .gph | Optimal | 11.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| keller6 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 3361 vars, density 0.000595, optimal no; QUBO, 3361 vars, density 0.182, optimal no | .gph | Best known | 59 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| mammalia-kangaroo-interactions | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 17 vars, density 0.118, optimal yes; QUBO, 17 vars, density 0.706, optimal yes | .gph | Optimal | 4.0 | Maximilian Schicker | 4 | Instance Solution Best submission | |
| p_hat1500-1 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 1500 vars, density 0.00133, optimal yes; QUBO, 1500 vars, density 0.747, optimal yes | .gph | Optimal | 12 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| p_hat1500-3 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 1500 vars, density 0.00133, optimal yes; QUBO, 1500 vars, density 0.247, optimal yes | .gph | Optimal | 94.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| sloane_1dc_128 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 128 vars, density 0.0156, optimal yes; QUBO, 128 vars, density 0.194, optimal yes | .gph | Optimal | 16.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| sloane_1dc_64 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 64 vars, density 0.0312, optimal yes; QUBO, 64 vars, density 0.292, optimal yes | .gph | Optimal | 10.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| sloane_1zc_128 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 128 vars, density 0.0156, optimal yes; QUBO, 128 vars, density 0.151, optimal yes | .gph | Optimal | 18.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| sloane_2dc_128 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 128 vars, density 0.0156, optimal yes; QUBO, 128 vars, density 0.642, optimal yes | .gph | Optimal | 5.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| socfb-haverford76 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 1446 vars, density 0.00138, optimal no; QUBO, 1446 vars, density 0.0583, optimal no | .gph | Optimal | 282.0 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| socfb-trinity100 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 2613 vars, density 0.000765, optimal no; QUBO, 2613 vars, density 0.0336, optimal no | .gph | Best known | 499 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| sorrell4 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 2048 vars, density 0.000977, optimal yes; QUBO, 2048 vars, density 0.241, optimal yes | .gph | Optimal | 24 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| sorrell7 | Maximum Independent Set | A graph benchmark that tests maximum-cardinality search on sparse and dense structures with known comparison points.Paper: MIP, 2048 vars, density 0.000977, optimal no; QUBO, 2048 vars, density 0.0198, optimal no | .gph | Best known | 189 | Maximilian Schicker | 2 | Instance Solution Best submission | |
| network05 | Network Design | A flow-routing benchmark for constructing sparse directed networks under traffic and degree constraints.Paper: MIP, 101 vars, density 0.0335, optimal yes; QUBO, 3640 vars, density 0.0527, optimal yes | Optimal | 65500.0 | Maximilian Schicker | 1 | Solution Best submission | ||
| network06 | Network Design | A flow-routing benchmark for constructing sparse directed networks under traffic and degree constraints.Paper: MIP, 181 vars, density 0.0202, optimal yes; QUBO, 6650 vars, density 0.036, optimal yes | Optimal | 101000.0 | Maximilian Schicker | 1 | Solution Best submission | ||
| network07 | Network Design | A flow-routing benchmark for constructing sparse directed networks under traffic and degree constraints.Paper: MIP, 295 vars, density 0.013, optimal yes; QUBO, 10982 vars, density 0.0262, optimal yes | Optimal | 142400.0 | Maximilian Schicker | 1 | Solution Best submission | ||
| network08 | Network Design | A flow-routing benchmark for constructing sparse directed networks under traffic and degree constraints.Paper: MIP, 449 vars, density 0.00887, optimal yes; QUBO, 16876 vars, density 0.0199, optimal yes | Optimal | 170231.0 | Maximilian Schicker | 1 | Solution Best submission | ||
| network09 | Network Design | A flow-routing benchmark for constructing sparse directed networks under traffic and degree constraints.Paper: MIP, 649 vars, density 0.00631, optimal yes; QUBO, 24572 vars, density 0.0157, optimal yes | Optimal | 196750.0 | Maximilian Schicker | 1 | Solution Best submission | ||
| network10 | Network Design | A flow-routing benchmark for constructing sparse directed networks under traffic and degree constraints.Paper: MIP, 901 vars, density 0.00465, optimal yes; QUBO, n/a vars, optimal yes | Optimal | 210800.0 | Maximilian Schicker | 1 | Solution Best submission | ||
| network11 | Network Design | A flow-routing benchmark for constructing sparse directed networks under traffic and degree constraints.Paper: MIP, 1211 vars, density 0.00352, optimal no; QUBO, n/a vars, optimal no | Best known | 240818.0 | Maximilian Schicker | 1 | Solution Best submission | ||
| network12 | Network Design | A flow-routing benchmark for constructing sparse directed networks under traffic and degree constraints.Paper: MIP, 1585 vars, density 0.00273, optimal no; QUBO, n/a vars, optimal no | Best known | 285643.0 | Maximilian Schicker | 1 | Solution Best submission | ||
| network13 | Network Design | A flow-routing benchmark for constructing sparse directed networks under traffic and degree constraints.Paper: MIP, 2029 vars, density 0.00216, optimal no; QUBO, n/a vars, optimal no | Best known | 313000.0 | Maximilian Schicker | 1 | Solution Best submission | ||
| network14 | Network Design | A flow-routing benchmark for constructing sparse directed networks under traffic and degree constraints.Paper: MIP, 2549 vars, density 0.00173, optimal no; QUBO, n/a vars, optimal no | Best known | 367899.0 | Maximilian Schicker | 1 | Solution Best submission | ||
| network15 | Network Design | A flow-routing benchmark for constructing sparse directed networks under traffic and degree constraints.Paper: MIP, 3151 vars, density 0.00142, optimal no; QUBO, n/a vars, optimal no | Best known | 397000.0 | Maximilian Schicker | 1 | Solution Best submission | ||
| network16 | Network Design | A flow-routing benchmark for constructing sparse directed networks under traffic and degree constraints.Paper: MIP, 3841 vars, density 0.00117, optimal no; QUBO, n/a vars, optimal no | Best known | 430000.0 | Maximilian Schicker | 1 | Solution Best submission | ||
| network17 | Network Design | A flow-routing benchmark for constructing sparse directed networks under traffic and degree constraints.Paper: MIP, 4625 vars, density 0.000978, optimal no; QUBO, n/a vars, optimal no | Best known | 488699.0 | Maximilian Schicker | 1 | Solution Best submission | ||
| network18 | Network Design | A flow-routing benchmark for constructing sparse directed networks under traffic and degree constraints.Paper: MIP, 5509 vars, density 0.000826, optimal no; QUBO, n/a vars, optimal no | Best known | 517398.0 | Maximilian Schicker | 1 | Solution Best submission | ||
| network19 | Network Design | A flow-routing benchmark for constructing sparse directed networks under traffic and degree constraints.Paper: MIP, 6499 vars, density 0.000704, optimal no; QUBO, n/a vars, optimal no | Best known | 565570.0 | Maximilian Schicker | 1 | Solution Best submission | ||
| network20 | Network Design | A flow-routing benchmark for constructing sparse directed networks under traffic and degree constraints.Paper: MIP, 7601 vars, density 0.000605, optimal no; QUBO, n/a vars, optimal no | Best known | 570974.0 | Maximilian Schicker | 1 | Solution Best submission | ||
| network21 | Network Design | A flow-routing benchmark for constructing sparse directed networks under traffic and degree constraints.Paper: MIP, 8821 vars, density 0.000523, optimal no; QUBO, n/a vars, optimal no | Best known | 641249.0 | Maximilian Schicker | 1 | Solution Best submission | ||
| network22 | Network Design | A flow-routing benchmark for constructing sparse directed networks under traffic and degree constraints.Paper: MIP, 10165 vars, density 0.000456, optimal no; QUBO, n/a vars, optimal no | Best known | 709597.0 | Maximilian Schicker | 1 | Solution Best submission | ||
| network23 | Network Design | A flow-routing benchmark for constructing sparse directed networks under traffic and degree constraints.Paper: MIP, 11639 vars, density 0.000399, optimal no; QUBO, n/a vars, optimal no | Best known | 749923.0 | Maximilian Schicker | 1 | Solution Best submission | ||
| network24 | Network Design | A flow-routing benchmark for constructing sparse directed networks under traffic and degree constraints.Paper: MIP, 13249 vars, density 0.000352, optimal no; QUBO, n/a vars, optimal no | Best known | 779410.0 | Maximilian Schicker | 1 | Solution Best submission | ||
| XSH-n20-k4-01 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-02 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4521 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-03 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4974 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-04 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4977 vars, density 0.0116, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-05 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4973 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-06 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-07 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-08 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4522 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-09 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4522 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-10 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-11 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-12 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-13 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4523 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-14 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4971 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-15 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4970 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-16 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-17 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-18 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4970 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-19 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4972 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-20 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4971 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-21 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4975 vars, density 0.0117, optimal yes | .vrp | Best known | 0 | Instance Solution | |||
| XSH-n20-k4-22 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-23 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4970 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-24 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4524 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-25 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-26 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4977 vars, density 0.0116, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-27 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-28 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4525 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-29 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-30 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4970 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-31 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4971 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-32 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4524 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-33 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-34 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4974 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-35 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4976 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-36 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-37 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-38 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-39 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-40 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-41 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-42 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-43 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-44 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-45 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4971 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-46 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4524 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-47 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4972 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-48 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4526 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-49 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-50 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-51 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4527 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-52 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4525 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-53 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4974 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-54 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4525 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| XSH-n20-k4-55 | Vehicle Routing | A logistics benchmark for route construction with tight capacity and service-window constraints.Paper: MIP, 441 vars, density 0.0116, optimal yes; QUBO, 4974 vars, density 0.0117, optimal yes | .vrp | Optimal | 0 | Instance Solution | |||
| topology_1000000_16 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove. | .dat | Best known | 0 | Instance Solution | |||
| topology_1000000_32 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove. | .dat | Best known | 0 | Instance Solution | |||
| topology_100000_8 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove. | .dat | Best known | 0 | Instance Solution | |||
| topology_1024_4 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove. | .dat | Best known | 0 | Instance Solution | |||
| topology_15_3 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove.Paper: MIP, 22261 vars, density 0.000209, optimal yes; QUBO, 45079 vars, density 0.00219, optimal yes | .dat | Optimal | 3.0 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| topology_15_4 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove.Paper: MIP, 22261 vars, density 0.000209, optimal yes; QUBO, 45094 vars, density 0.00219, optimal yes | .dat | Optimal | 2.0 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| topology_1726_30 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove. | .dat | Best known | 0 | Instance Solution | |||
| topology_20_3 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove.Paper: MIP, 72581 vars, density 6.53e-05, optimal yes; QUBO, 146535 vars, density 0.00122, optimal yes | .dat | Optimal | 3.0 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| topology_20_4 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove.Paper: MIP, 72581 vars, density 6.53e-05, optimal yes; QUBO, 146555 vars, density 0.00122, optimal yes | .dat | Optimal | 3.0 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| topology_20_5 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove.Paper: MIP, 72581 vars, density 6.53e-05, optimal yes; QUBO, 146555 vars, density 0.00122, optimal yes | .dat | Optimal | 2.0 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| topology_25_3 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove.Paper: MIP, 180601 vars, density 2.65e-05, optimal yes; QUBO, n/a vars, optimal yes | .dat | Optimal | 4.0 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| topology_25_4 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove.Paper: MIP, 180601 vars, density 2.65e-05, optimal yes; QUBO, n/a vars, optimal yes | .dat | Optimal | 3.0 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| topology_25_5 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove.Paper: MIP, 180601 vars, density 2.65e-05, optimal yes; QUBO, n/a vars, optimal yes | .dat | Optimal | 3.0 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| topology_25_6 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove.Paper: MIP, 180601 vars, density 2.65e-05, optimal yes; QUBO, n/a vars, optimal yes | .dat | Optimal | 2.0 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| topology_30_4 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove.Paper: MIP, 379321 vars, density 1.27e-05, optimal yes; QUBO, n/a vars, optimal yes | .dat | Optimal | 3.0 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| topology_30_5 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove.Paper: MIP, 379321 vars, density 1.27e-05, optimal yes; QUBO, n/a vars, optimal yes | .dat | Optimal | 3.0 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| topology_30_6 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove.Paper: MIP, 379321 vars, density 1.27e-05, optimal no; QUBO, n/a vars, optimal no | .dat | Best known | 3.0 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| topology_35_5 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove.Paper: MIP, 709241 vars, density 6.84e-06, optimal no; QUBO, n/a vars, optimal no | .dat | Best known | 4.0 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| topology_35_6 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove.Paper: MIP, 709241 vars, density 6.84e-06, optimal no; QUBO, n/a vars, optimal no | .dat | Best known | 3.0 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| topology_40_6 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove.Paper: MIP, 1218361 vars, density 4e-06, optimal yes; QUBO, n/a vars, optimal yes | .dat | Optimal | 5.0 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| topology_4855_15 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove. | .dat | Best known | 0 | Instance Solution | |||
| topology_50_4 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove.Paper: MIP, 3003701 vars, density 1.63e-06, optimal no; QUBO, n/a vars, optimal no | .dat | Best known | 5.0 | Maximilian Schicker | 3 | Instance Solution Best submission | |
| topology_512_4 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove. | .dat | Best known | 0 | Instance Solution | |||
| topology_512_6 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove. | .dat | Best known | 0 | Instance Solution | |||
| topology_65536_6 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove. | .dat | Best known | 0 | Instance Solution | |||
| topology_9344_6 | Topology Design | A graph-design benchmark where a small diameter improvement can be meaningful and hard to prove. | .dat | Best known | 0 | Instance Solution |