Newsroom

Highlights of Gurobi Optimizer 6.0

Houston, Texas – November 11th, 2014 Gurobi Optimization, Inc. is pleased to announce the release of Gurobi Optimizer 6.0. With this new version, Gurobi continues our focus on delivering the significant improvements with each version our users have come to expect.

Newsroom

Highlights of Gurobi Optimizer 6.0

Houston, Texas – November 11th, 2014 Gurobi Optimization, Inc. is pleased to announce the release of Gurobi Optimizer 6.0. With this new version, Gurobi continues our focus on delivering the significant improvements with each version our users have come to expect.

Newsroom

Highlights of Gurobi Optimizer 6.0

Houston, Texas – November 11th, 2014 Gurobi Optimization, Inc. is pleased to announce the release of Gurobi Optimizer 6.0. With this new version, Gurobi continues our focus on delivering the significant improvements with each version our users have come to expect.

Houston, Texas – November 11th, 2014 Gurobi Optimization, Inc. is pleased to announce the release of Gurobi Optimizer 6.0. With this new version, Gurobi continues our focus on delivering the significant improvements with each version our users have come to expect.

Gurobi Optimization, Inc. is pleased to announce the release of Gurobi Optimizer 6.0. With this new version, Gurobi continues our focus on delivering the significant improvements with each version our users have come to expect.

 

Some of the enhancements and the new features include:

Significant performance improvements across MIP, LP, MIQP, and MIQCP problem types

New Distributed MIP Solver – Users can now harness the power of multiple independent machines to solve a MIP model in parallel

Algorithmic support for piecewise-linear objectives – Users can now explicitly model piecewise-linear objective functions, and the simplex solver can now exploit this structure to solve such models faster

Additional features users have asked for such as:

Concurrent LP solver – we’ve extended our existing distributed concurrent solver to support both LP and MIP models

Support for two billion non-zeros – Users can now build and solver models whose constraint matricies have more than two billion non-zero values

Explicit support for lazy constraints – Users can use the new lazy constraint attribute to mark constraints as lazy

Other improvements include new asynchronous optimization methods, a new scaling option, and more

Start Solving with Gurobi

Try Gurobi on your own optimization models and see how it performs on real decision problems.

Start Solving with Gurobi

Try Gurobi on your own optimization models and see how it performs on real decision problems.

Start Solving with Gurobi

Try Gurobi on your own optimization models and see how it performs on real decision problems.