The release of the Gurobi Optimizer 4.6 enhances performance, fills key feature gaps, and delivers on requested features

Gurobi Optimization, Inc. is pleased to announce the release of Gurobi Optimizer 4.6. This new release features enhanced Python modeling capabilities, significant improvements in primal simplex performance, improved MIP performance and robustness, and a new sifting algorithm for LP models that have many more variables than constraints.

The new release is available from the Gurobi web site.

This update includes:

  • An expanded Python modeling interface that makes it easier to build concise and efficient models.
  • Substantial performance improvements in our primal simplex and MIQP solvers.
  • Improved MIP performance.
  • Substantial improvements in MIP robustness: small tolerance violations are much less likely.
  • A new sifting algorithm for LP models with many more variables than constraints.
  • Support for user branching priorities in MIP.
  • A new presolve sparsify option that can substantially reduce the difficulty of some MIP models.
  • A new zero objective heuristic for finding feasible solutions to difficult MIP models.
  • Support for reading .zip and .7zip files.

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