Gurobi Introduces Gurobi Optimizer v7.0, with Higher Performance and Powerful New Modeling Capabilities, Plus a Significant Upgrade to the Gurobi Instant Cloud

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Gurobi Optimization has released version 7.0 of its leading mathematical programming solver, with significant performance improvements and several major new features, including enhanced Python modeling capabilities, support for multiple objectives, support for solution pools, and automatic linearization of many common higher-level modeling constructs through our new general constraint interface.

“V7.0 continues our history of delivering significant performance improvements each year, along with the addition of new features to help our users quickly develop models and then turn them into applications,” said Dr. Rothberg, Gurobi CEO and co-founder.

Gurobi Optimization has released version 7.0 of its leading mathematical programming solver, with significant performance improvements and several major new features, including enhanced Python modeling capabilities, support for multiple objectives, support for solution pools, and automatic linearization of many common higher-level modeling constructs through our new general constraint interface.

“Version 7.0 continues our history of delivering significant performance improvements each year, along with the addition of new features to help our users quickly develop models and then turn them into full-featured applications,” said Dr. Edward Rothberg, CEO and co-founder of Gurobi Optimization.

Significant Performance Improvements

Performance testing using Gurobi’s test library, consisting of literally thousands of real-world models, shows significant improvements versus the already industry-leading Gurobi v6.5. Specifically, for mixed-integer programming models, v7.0 is 19% faster overall and 30% faster on difficult models that take more than 100s to solve. For linear programming models, v7.0 is 10% faster overall, a very notable improvement for this class of models. In addition, v7.0 is 46% faster on quadratically constrained programming (QCP) models and 48% faster on MIQCP models.

New Modeling and Solution Capabilities

The Gurobi Python API has been enhanced with new methods and classes that further simplify the task of translating mathematical models into efficient implementations. In addition, the new release includes several major new capabilities that are available from all of Gurobi’s APIs:

  • multi-objective optimization, which allows you to specify multiple objectives and their relative priorities
  • general constraints, which allow you to input commonly occurring constraints (min/max, abs, and/or, and indicator constraints) and have the solver linearize them for you
  • solution pools, which allow you to obtain more than just one optimal solution to a MIP model

Expanded Python Support for the Mac

With the addition of Python 3.5 support on the Mac, Gurobi Optimizer v7.0 now supports all of the most popular Python platforms. You can also now use Gurobi with the Anaconda Python 3.5 distribution for the Mac, with its large number of pre-built libraries to support full application development and included Jupyter Notebook development environment.

Significantly Enhanced Gurobi Instant Cloud

The Gurobi Instant Cloud has undergone a significant set of upgrades. These include enhanced API support for easier launching of cloud instances directly from Gurobi APIs, a more intuitive online interface to help users get up and running more quickly, and new machine pool support which simplifies the launching and management of cloud machines.

About Gurobi

Gurobi (http://www.gurobi.com) is in the business of helping companies make better decisions through the use of prescriptive analytics. In addition to providing the best math programming solver, as well as tools for distributed optimization and optimization in the cloud, the company is known for its outstanding support and no-surprises pricing.

The Gurobi Optimizer is a state-of-the-art solver for linear programming (LP), quadratic programming (QP), quadratically constrained programming (QCP), mixed-integer linear programming (MILP), mixed-integer quadratic programming (MIQP), and mixed-integer quadratically constrained programming (MIQCP). Gurobi was designed from the ground up to exploit modern architectures and multi-core processors, using the most advanced implementations of the latest algorithms. Founded in 2008, Gurobi Optimization is based in Houston, TX (+1 713 871 9341).

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