Prior Version Enhancements
This page lists a summary of enhancements for recent versions. Please contact us if you'd like to discuss your specific needs, get help upgrading, or have questions to help you get the most of the lastest version of the Gurobi Optimizer.
If you would like to see a new feature or have us enhance an existing features we'd love to hear from you.
Enhancement Summary for 4.6
This 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.
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 as well as
- 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
Enhancement Summary for 4.5
The Gurobi Optimizer 4.5 continues to outperform other optimization solvers on important industry benchmarks. Gurobi consistently solves optimization models faster than either CPLEX or XPRESS, whether using 1, 4, or 12 processing cores. In some benchmarks, Gurobi mean performance is more than 8 times that of the competition. In addition, when time limits are imposed on solution time, Gurobi often finds solutions where other solvers cannot. Faster solution times and greater reliability are important features in an optimization solver, leading to more useful results and increased user productivity.
Professor Hans Mittelmann of Arizona State University publishes a set of standard benchmark results for a wide range of optimization solvers and optimization problem types. On eight different benchmarks, data is provided that allows one to compare the performance of the three leading commercial optimization solvers on linear and mixed-integer programming problems. When the full spectrum of benchmarks is considered, a clear picture emerges: Gurobi Optimizer 4.5 consistently outperforms CPLEX 220.127.116.11 and XPRESS 7.2. Details on these benchmarks can be found on Professor Mittelmann's website, Benchmarks for Optimization Software.
Gurobi dominates on mixed-integer-programming (MIP) benchmarks
- MIPLIB 2010: On this important new benchmark that measures the time to obtain proven optimal solutions for a set of 87 MIP models, Gurobi outperforms both CPLEX and XPRESS, whether using 1 or 12 CPU cores.
- Feasibility: In benchmark tests that measure how long it takes to find the first feasible solution to a MIP problem, Gurobi dominates the competition, finding solutions more than 3 times as fast as CPLEX on average and more than 8 times as fast as XPRESS.
- Infeasibility: Proving a MIP model has no feasible solution can be an important step in model development. Gurobi solves this problem 20% faster than CPLEX and 80% faster than XPRESS.
- Pathological: While Gurobi does not win this benchmark, Professor Mittelmann states: "This benchmark is not giving a representative impression of the relative performance of the codes". The benchmark tests are presented simply to show that even the best MIP codes can exhibit pathologically bad behavior. Because the set of tested models changes frequently, the "winners" for this benchmark are subject to change.
- MIQP: In benchmark tests that measure performance on mixed-integer programming models with quadratic objective functions, Gurobi dominates the competition, solving these models more than twice as fast on average.
Enhancements Summary for 4.0
The Gurobi Optimizer 4.0 provides Quadratic Programming solvers, improved performance and many new features:
- New QP and MIQP solvers: New quadratic programming solvers let you to formulate and solve optimization models with convex quadratic objectives. For continuous quadratic programming (QP) problems, the new release includes primal and dual simplex QP algorithms as well as a parallel barrier QP solver. A deterministic, parallel branch-and-cut algorithm is available for solving mixed integer quadratic programming (MIQP) models. All deliver industry leading performance.
- Continued performance improvements in MIP, dual simplex, and barrier: Initial tests indicate performance speed-ups of roughly 15% for MIP, 10% for simplex, and 10% for barrier.
- Improved numerical robustness: Several improvements in numerical robustness lead to better handling of numerically difficult models when using the simplex, barrier, and MIP solvers.
- Concurrent LP: When this feature is enabled, the solver will automatically pick the best algorithm for solving a given LP model. Both deterministic and non-deterministic versions are available. The deterministic solver guarantees identical solution paths over multiple runs. The non-deterministic solver does not give this guarantee, though solution-path differences are relatively rare, and the overall performance of the non-deterministic version is superior to that of the deterministic solver.
- New delayed MIP strategy option: With this option, you can direct Gurobi to reset the MIP solution focus to improving the best feasible solution once a specified time limit or objective gap has been reached.
- Visual Studio 2010 support: On Microsoft Windows, you can now call Gurobi from the latest version of Visual Studio.
- More explicit control over floating license use in Java and .NET: You can now explicitly release a license token, rather than relying on the system garbage collector to release it for you.