Visualizations of Mittelmann benchmarks

Interactive plots showing pairwise time differences for every instance and every solver

Interactive charts comparing the results of Hans Mittelmann’s benchmarks. Each solver can be selected to show pairwise running time factors for every other solver in the respective benchmark. These plots should make browsing the results easier. The score (scaled shifted geometric mean) is recomputed using the reported solving times. We also calculate a “virtual best” or “ensemble” solver that picks the best performance over all solvers for every single problem instance. This might reveal how much potential the individual solvers still have. Please let me know if you have a question or if there is an error.

LPfeas Benchmark (find PD feasible point) (9 Jun 2024)

Choose base solver for comparison:

solver score (as reported) solved of 65
⭐ virtual best 0.74 100%
🥇 COPT 7.1.0 1.00 (1.00) 100%
🥈 Gurobi 11.0.0 1.29 (1.29) 97%
🥉 Optverse 0.7.3 1.72 (1.72) 95%
📊 MindOpt 1.0.0 1.81 (1.81) 97%
📊 MOSEK 10.1.9 2.62 (2.62) 98%
📊 XOPT 0.0.6 5.41 (5.41) 91%
📊 ORTOOLS 9.10 15.66 (15.70) 77%
📊 KNITRO 14.0.0 19.34 (19.30) 74%
📊 HiGHS 1.6.0 19.35 (19.30) 80%
📊 MATLAB R2023a 27.07 (27.10) 77%
📊 Tulip 0.9.4 66.89 (66.90) 55%
previous benchmarks 🔽

LPopt Benchmark (find optimal basic solution) (20 May 2024)

Choose base solver for comparison:

solver score (as reported) solved of 65
⭐ virtual best 0.78 100%
🥇 COPT 7.1.0 1.00 (1.00) 100%
🥈 Gurobi 11.0.0 1.58 (1.58) 97%
🥉 Optverse 0.7.3 1.80 (1.80) 95%
📊 MindOpt 1.0.0 1.89 (1.89) 97%
📊 MOSEK 10.1.9 5.61 (5.61) 80%
📊 XOPT 0.0.6 6.33 (6.33) 80%
📊 HiGHS 1.6.0 17.44 (17.40) 78%
📊 CLP 1.17.7 26.13 (26.10) 62%
📊 Google-GLOP 56.97 (57.00) 51%
📊 SOPLEX 6.0.0 87.20 (87.20) 49%
previous benchmarks 🔽

Large Network-LP Benchmark (commercial vs free) (18 Apr 2024)

Choose base solver for comparison:

solver score (as reported) solved of 25
⭐ virtual best 0.87 100%
🥇 OptVerse 0.7.2 1.00 (1.00) 100%
🥈 MindOpt 1.0.0 1.17 (1.17) 100%
🥉 Gurobi 11.0.0 1.68 (1.68) 100%
📊 COPT 7.1.0 2.14 (2.14) 100%
📊 Clp 1.17.7 6.03 (6.04) 100%
📊 HiGHS 1.7.0 12.84 (12.80) 80%
📊 MOSEK 10.1.9 26.69 (26.70) 88%
📊 QSopt 1.01 35.26 (35.30) 68%
📊 SOPLEX 6.0.0 67.04 (67.00) 64%
previous benchmarks 🔽

The MIPLIB2017 Benchmark Instances and Robustness Check - 8 threads (14 Jul 2024)

Choose base solver for comparison:

solver score (as reported) solved of 240
⭐ virtual best 0.58 99%
🥇 Gurobi 11.0.0 1.00 (1.00) 95%
🥈 COPT 7.1.0 1.44 (1.44) 92%
🥉 TAYLORMIP 0.8 2.45 (2.45) 95%
📊 OptVerse 0.7.0 2.88 (2.88) 84%
📊 LEOPT 0.5.1 4.17 (4.18) 75%
📊 MindOpt 1.0.0 4.18 (4.18) 82%
📊 XOPT 0.0.6 6.34 (6.34) 78%
📊 HiGHS 1.6.0 9.98 (9.98) 66%
📊 SCIPC 10.59 (10.60) 63%
📊 SCIP/spx 9.0.0 12.39 (12.40) 58%
📊 CBC 2.10.5 18.42 (18.40) 45%
previous benchmarks 🔽

MILP cases that are slightly pathological (17 Jul 2024)

Choose base solver for comparison:

solver score (as reported) solved of 45
⭐ virtual best 0.49 100%
🥇 GUROBI 11.0.0 1.00 (1.00) 98%
🥈 COPT 7.1.0 2.22 (2.22) 91%
🥉 OptVerse 0.7.0 4.59 (4.59) 82%
📊 TAYLORMIP 0.8 5.43 (5.43) 76%
📊 XOPT 0.0.6 15.71 (15.70) 69%
📊 MindOpt 1.0.0 20.82 (20.80) 53%
📊 HiGHS 1.7.0 23.51 (23.50) 64%
📊 LEOPT 0.5.1 24.81 (24.80) 60%
📊 SCIPC 25.89 (25.90) 62%
📊 SCIP 9.0.0 44.65 (44.70) 44%
📊 CBC 2.10.7 78.31 (78.30) 22%
📊 GLPK 5.0 82.99 (83.00) 13%
previous benchmarks 🔽

Infeasibility Detection for MILP Problems (5 Apr 2024)

Choose base solver for comparison:

solver score (as reported) solved of 32
⭐ virtual best 0.99 94%
🥇 GUROBI 11.0.0 1.00 (1.00) 94%
🥈 COPT 7.1.0 1.37 (1.37) 94%
🥉 OptVerse 0.7.0 3.00 (3.00) 88%
📊 MindOpt 1.0.0 7.81 (7.81) 84%
📊 XOPT 0.0.6 7.82 (7.82) 78%
📊 SCIPC 7.96 (7.96) 78%
📊 HiGHS 1.7.0 8.26 (8.26) 78%
📊 SCIP 9.0.0 10.67 (10.30) 81%
📊 CBC 2.10.5 22.51 (22.50) 62%
previous benchmarks 🔽

Several SDP-codes on sparse and other SDP problems (8 Feb 2024)

Choose base solver for comparison:

solver score (as reported) solved of 75
⭐ virtual best 0.61 100%
🥇 COPT 7.1.0 1.00 (1.00) 100%
🥈 MindOpt 1.0.0 1.45 (1.45) 100%
🥉 MOSEK 10.1.23 3.67 (3.67) 97%
📊 SDPT3 4.0 5.18 (5.18) 92%
📊 CSDP 6.2.0 5.25 (5.25) 93%
📊 HDSDP 1.0.0 7.91 (7.91) 93%
📊 SDPA 7.4.2 10.52 (10.50) 81%
📊 SeDuMi 1.3.5 29.06 (29.10) 83%
previous benchmarks 🔽

Large Second Order Cone Benchmark (8 Apr 2024)

Choose base solver for comparison:

solver score (as reported) solved of 18
⭐ virtual best 0.79 100%
🥇 COPT 7.1.0 1.00 (1.00) 100%
🥈 MOSEK 10.1.28 1.04 (1.04) 100%
🥉 Gurobi 11.0.0 1.11 (1.11) 100%
📊 KNITRO 14.0.0 9.24 (9.24) 94%
📊 ECOS 2.0.4 97.22 (97.20) 61%
previous benchmarks 🔽

Mixed-integer SOCP Benchmark (9 Apr 2024)

Choose base solver for comparison:

solver score (as reported) solved of 47
⭐ virtual best 0.98 100%
🥇 Gurobi 11.0.0 1.00 (1.00) 100%
🥈 COPT 7.1.0 2.20 (2.16) 98%
🥉 MOSEK 10.1.28 10.89 (9.03) 77%
📊 SCIP 9.0.0 25.64 (20.10) 66%
previous benchmarks 🔽

Binary Non-Convex QPLIB Benchmark (28 Jun 2024)

Choose base solver for comparison:

solver score (as reported) solved of 97
⭐ virtual best 0.76 100%
🥇 Gurobi 11.0.0 1.00 (1.00) 97%
🥈 SHOT 1.1 1.29 (1.29) 94%
🥉 Baron 24.5.8 7.60 (7.60) 66%
📊 RAPOSa 4.4.1 13.20 (13.20) 71%
📊 SCIP 9.0.0 40.75 (40.70) 35%
📊 ANTIGONE 1.1 81.19 (81.20) 16%
📊 COUENNE 0.5 96.45 (96.40) 6%
previous benchmarks 🔽

Nonconvex QUBO-QPLIB Benchmark (9 Jun 2024)

Choose base solver for comparison:

solver score (as reported) solved of 23
⭐ virtual best 0.94 70%
🥇 QuBowl 1.00 (1.00) 65%
🥈 Gurobi 11.0.0 1.56 (1.30) 57%
🥉 Baron 24.5.8 1.82 (1.82) 52%
📊 SHOT 1.1 1.92 (1.76) 52%
📊 McSparse 2.0 2.36 (2.36) 52%
📊 SCIP 9.0 5.73 (5.73) 30%
📊 Biqbin 7.63 (5.40) 39%
previous benchmarks 🔽

Discrete Non-Convex QPLIB Benchmark (non-binary) (1 Jul 2024)

Choose base solver for comparison:

solver score (as reported) solved of 105
⭐ virtual best 0.62 99%
🥇 Gurobi 11.0.0 1.00 (1.00) 94%
🥈 SHOT 1.1 1.44 (1.44) 90%
🥉 Baron 24.5.8 6.86 (6.86) 69%
📊 SCIP 9.0.0 37.33 (37.30) 39%
📊 ANTIGONE 1.1 88.68 (88.70) 27%
previous benchmarks 🔽

Continuous Non-Convex QPLIB Benchmark (6 Jul 2024)

Choose base solver for comparison:

solver score (as reported) solved of 64
⭐ virtual best 0.30 95%
🥇 GUROBI 11.0.0 1.00 (1.00) 70%
🥈 Baron 24.5.8 2.68 (2.68) 55%
🥉 MINOTAUR 0.4.0 7.24 (7.24) 39%
📊 ANTIGONE 1.1 7.76 (7.76) 41%
📊 SCIP 9.0.0 12.17 (12.20) 23%
previous benchmarks 🔽

Convex Continuous QPLIB Benchmark (ext) (12 Mar 2024)

Choose base solver for comparison:

solver score (as reported) solved of 42
⭐ virtual best 0.73 100%
🥇 COPT 7.1.0 1.00 (1.00) 100%
🥈 OptVerse 0.7.0 1.28 (1.28) 98%
🥉 KNITRO 14.0.0 1.49 (1.49) 98%
📊 Gurobi 11.0.0 1.59 (1.59) 98%
📊 MOSEK 10.1.21 1.60 (1.60) 98%
📊 MindOpt 1.0.0 5.74 (5.43) 81%
📊 IPOPT 3.14.5 8.95 (8.95) 83%
📊 Mnotaur 45.32 (45.00) 60%
previous benchmarks 🔽

Convex Discrete QPLIB Benchmark (11 Jun 2024)

Choose base solver for comparison:

solver score (as reported) solved of 31
⭐ virtual best 0.68 87%
🥇 GUROBI 11.0.0 1.00 (1.00) 81%
🥈 COPT 7.1.0 1.00 (1.00) 77%
🥉 Shot 1.1 1.07 (1.07) 81%
📊 Baron 24.5.8 4.28 (4.28) 65%
📊 MOSEK 10.1.23 10.05 (10.00) 58%
📊 KNITRO 14.0.0 13.63 (13.60) 52%
📊 SCIP 9.0.0 21.82 (21.80) 45%
📊 Bonmin 1.8.7 54.64 (54.60) 23%
📊 MNTAUR 63.99 (64.00) 26%
previous benchmarks 🔽

Mixed Integer Nonlinear Programming Benchmark (MINLPLIB) (8 Jun 2024)

Choose base solver for comparison:

solver score (as reported) solved of 87
⭐ virtual best 0.35 97%
🥇 BARON 1.00 (0.10) 93%
🥈 SHOT 2.89 (0.20) 62%
🥉 SCIP 4.64 (0.30) 75%
📊 LINDO 15.43 (0.80) 48%
📊 ANTIGONE 18.47 (1.00) 61%
📊 COUENNE 42.18 (2.30) 28%
previous benchmarks 🔽

MPEC Benchmark (Math. Progr. w. Equilibrium Constraints) (9 Feb 2024)

Choose base solver for comparison:

solver score (as reported) solved of 29
⭐ virtual best 1.00 93%
🥇 KNITRO 14.0 1.00 (1.00) 93%
🥈 filter MPEC 16.90 (16.90) 62%
🥉 LOQO 7.03 37.10 (37.10) 21%
previous benchmarks 🔽