CPLEX Optimizer performance

IBM ILOG CPLEX Optimizer confirms its performance leadership with the Version 12.8 CPLEX Optimizer which provides breakthrough improvements in performance.

Mixed integer programming models with linear terms are solved by the CPLEX MIP Optimizer 23% faster on average for large problems (>1000s) than version 12.7. For models with convex quadratic terms, improvements on larger models (>10s) is 13% on average. On a special model type called QDP/SOCP the speedup is between 5-10% on average.

CPLEX Optimizer

CPLEX MILP Performance evolution

CPLEX MILP performance evolution

CPLEX 12.7.0 vs 12.8.0: LP performance improvement

CPLEX 12.7.0 vs 12.8.0: LP performance improvement

CPLEX 12.7.0 vs 12.8.0: MILP performance improvement

CPLEX 12.7.0 vs 12.8.0: MILP performance improvement

CPLEX 12.7.0 vs 12.8.0: MIQP performance improvement

CPLEX 12.7.0 vs 12.8.0: MIQP performance improvement

CPLEX 12.7.0 vs 12.8.0:QCP/SOCP performance improvement

CPLEX 12.7.0 vs 12.8.0:QCP/SOCP performance improvement

CPLEX CP Optimizer

Performance Trends

Test-cases are separated into two groups: 

  1. Integer problems, which include rostering, matching, sports scheduling problems. 
  2. Scheduling problems, which include resource-constrained and job-shop scheduling problems. 

On performance of purely combinatorial/integer problems a breakthrough speed improvement has been achieved with a staggering +2.5x, i.e. a more than 150% faster solve. On purely scheduling problems with COS 12.8, we have achieved a 10% speedup compared to 12.7.1. Compared to 12.7.0, a 50% speed up can be observed.

CPLEX CP Optimizer

CP Optimizer - integer/scheduling performance evolution

CP Optimizer - integer/scheduling performance evolution

CP Optimizer – Performance improvements

CP Optimizer – performance improvements

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