Ibm ilog cplex optimization studio 12.7 student download
Alameddine, Anew reformulation-linearization technique for bilinear programming problems, Journal of Global Optimization 2, 379-410, 1992. 2016 IBM Corporation8 0 0,2 0,4 0,6 0,8 1 >0s >1s >10s 1,00 1,00 1,00 0,79 0,61 0,35 CPLEX 12.6.3 CPLEX 12.7.0 Date: 6 November 2016 Testset: Non-convex (MI)QP: 742 models Machine: Intel X5650 2.67GHz, 24 GB RAM, 12 threads, deterministic Timelimit: 10,000 sec Non-convex (MI)QP (12 threads) 447 models 79 models Time limits: 12 / 1 1.27x 2.83x1.65x 157 models All the improvement comes from newly added cutting planes: Cuts from Reformulation- Linearization Techniques (RLT-cuts) - H. 2016 IBM Corporation7 0 0,2 0,4 0,6 0,8 1 >0s >1s >10s 1,00 1,00 1,00 0,92 0,87 0,78 CPLEX 12.6.3 CPLEX 12.7.0 Date: 6 November 2016 Testset: MIQCP: 326 models Machine: Intel X5650 2.67GHz, 24 GB RAM, 12 threads, deterministic Timelimit: 10,000 sec CPLEX 12.6.3 vs.12.7.0: MIQCP performance improvement Convex MIQCP (12 threads) 259 models 88 models Time limits: 5 / 1 1.09x 1.29x1.15x 154 models MIQCP improvements summary (all for Outer Approximation, OA) -More aggressive reliability branching: 4% -Better synchronization of cone cuts in the tree: 2% -Better integration between cone cuts and QCP relaxation: Repeatedly solve QCP relaxation at the root, to refine OA with cone cuts: 4% Solve QCP relaxation in the tree, to refine the OA with cone cuts: 10% 2016 IBM Corporation6 0 0,2 0,4 0,6 0,8 1 >0s >1s >10s 1,00 1,00 1,00 0,94 0,89 0,85 CPLEX 12.6.3 CPLEX 12.7.0 Date: 6 November 2016 Testset: MIQP: 365 models Machine: Intel X5650 2.67GHz, 24 GB RAM, 12 threads, deterministic Timelimit: 10,000 sec CPLEX 12.6.3 vs.12.7.0: MIQP performance improvement Convex MIQP (12 threads) 263 models 84 models Time limits: 3 / 0 1.07x 1.18x1.13x 123 models MIQP improvements summary -Node cuts (separation and filtering): 7% -Tuning of node probing effort: 6%
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2016 IBM Corporation5 0 0,2 0,4 0,6 0,8 1 >1s >10s >100s 1,00 1,00 1,00 0,92 0,90 0,85 CPLEX 12.6.3 CPLEX 12.7.0 Date: 6 November 2016 Testset: MILP: 4256 models Machine: Intel X5650 2.67GHz, 24 GB RAM, 12 threads, deterministic Timelimit: 10,000 sec Deterministic parallel MILP (12 threads) 2048 models 667 models Time limits: 55 / 24 1.08x 1.18x1.11x 1254 models MILP improvements summary -Root presolve: 2% -Propagation of implications in node presolve: 3% -Root and node probing: 3% -Node cuts (separation and filtering): 3% -Heuristics: 2% -Branching: 3% CPLEX 12.6.3 vs.12.7.0: MILP performance improvement
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2016 IBM Corporation IBM CPLEX Optimizer 12.7 Benders decomposition, Modeling Assistance, Xavier Nodet, Program Manager, CPLEX Optimization Studio