Features and Benefits

Designed to be the best end-to-end offering available

The opportunity to build a new product from scratch, unencumbered by legacy constraints, doesn’t come along every day. That was the opportunity presented to the optimization experts that founded Gurobi Optimization. Their goal was not only to deliver a truly superior solver, but also to provide everything that goes around that to help users succeed in building optimization models and deploying applications.

An Optimization Solver Reimagined

The Gurobi Optimizer was designed from the ground up to be the fastest, most powerful solver available for your LP, QP, QCP, and MIP (MILP, MIQP, and MIQCP) problems. In particular:

  • Our code was built to fully exploit parallelism. It's not a sequential code that was parallelized, but a fundamentally parallel code that you can also choose to run sequentially
  • Our MIP cutting plane routines are second to none. Not only do we have cutting-edge versions of all the standard cutting planes, but we’ve also gone beyond that to develop new classes of cuts available only in Gurobi
  • Our advanced MIP heuristics for quickly finding feasible solutions often produce good quality solutions where other solvers fall flat, leading to some of our biggest wins vs. the competition
  • Our barrier algorithms fully exploit the features of the latest computer architectures

The end result is that Gurobi's solvers consistently delivers outstanding performance across a full range of problems, with performance improving as your problems get harder. While benchmark results can fluctuate over time as competitors introduce new versions, Gurobi typically leads solver results across all tests over time. In addition, Gurobi recently solved 11 previously unsolvable models in the MIPLIB2010 library and has improved performance by 5X for LP problems and 13X for MIP problems over just the last four years.

Below is a short summary of Gurobi's features and benefits. For further information on Gurobi and how it can meet your specific needs, we invite you to contact us about your specific needs. We're happy to discuss how Gurobi can help you succeed in applying optimization to your applications.

Benefit Detail
Outstanding Solve Times
  • Gurobi has a history of leading public benchmarks and making continual improvements across a range of problem types.
  • You can see the standard public benmarks, maintained by Hans Mittelmann at Arizona State University, here: http://plato.la.asu.edu/bench.htm. Several benchmarks are given for LP, QP, MILP, and MIQP.
Support for common problem types
  • Linear Programming (LP)
  • Quadratic Programming (QP)
  • Quadratically Constrained Programming (QCP)
  • Mixed-Integer Linear Programming (MILP)
  • Mixed-Integer Quadratic Programming (MIQP)
  • Mixed-Integer Quadratically Constrained Programming (MIQCP)
Extremely robust code for confidence in results
  • Robustness has several components: numerical stability, correctness of results, scalability with problem size and difficulty, and robustness of solve times over a range of model instances.
  • Gurobi has a proven track record on all of these components.
  • Gurobi is tested thoroughly for numerical stability and correctness using an internal library of over 8000 models from industry and academia.
  • Gurobi recently solved eleven (11) challenge models not previously solved by other solvers. See http://miplib.zib.de.
  • Gurobi is tuned to optimize performance over a wide-range of instances
Easily model and develop applications with Python
  • Python interactive interface provides a powerful prototyping and application development tool
  • Simple language extensions and documented best practices provide many of the features of standard modeling languages, but inside a general programming language
  • Pre-built Python libraries support full application development
  • Save time and effort with a single integrated environment for both modeling and deployment
Range of programming and modeling language support
  • Object-oriented interfaces for C++, Java, .NET, and Python
  • Matrix-oriented interfaces for C, MATLAB, and R
  • Consistent intuitive design across all programming interfaces
  • Interfaces are lightweight, meaning that they are faster and use less memory
  • Links to standard modeling languages: AMPL, GAMS, AIMMS, and MPL
  • Links to Excel through Frontline Solvers
Flexible licensing to meet your needs
  • Each license can be used for both development and deployment
  • Each license can run multiple applications
  • Licenses can be transferred from consulting developer to end user
  • Licenses never depend on the number of cores per chip, meaning that the power of your license grows as chip capabilities grow
Outstanding support you can actually reach
  • Direct access to experienced, PhD-trained optimization experts, backed by the Gurobi development team, meaning no hunting for the right person to answer your questions
General Features Detail
Advanced implementations of the latest algorithms
  • LP Solver: primal and dual simplex algorithms, parallel barrier algorithm with crossover, concurrent optimization, and sifting algorithm
  • QP Solver: simplex and parallel barrier algorithms
  • QCP Solver: parallel SOCP barrier algorithm
  • MIP Solver: deterministic, parallel branch-and-cut, non-traditional tree-of-trees search, multiple default heuristics, solution improvement, cutting planes, and symmetry detection
Continuous model features
  • Deterministic and non-deterministic concurrent optimizers that allow you to exploit all of the cores in your machine
  • Multiple simplex pricing options, including steepest edge, devex, and partial pricing
  • Multiple barrier fill-reducing ordering options, including approximate minimum degree and vertex separator nested dissection
  • Both homogeneous and standard barrier algorithms
  • Multiple options for selecting the initial barrier crossover basis
  • Simplex warm starting using advanced bases or solution vectors
  • Automated sifting approach for large aspect ratio models
  • Efficient Irreducible Infeasible Subsystem (IIS) detection
  • Feasibility relaxation feature for minimizing constraint violations for infeasible models
  • Unbounded ray computation for unbounded models
  • Infeasibility proof computation for infeasible models
  • Detailed sensitivity information
Mixed-Integer model features
  • Sixteen different types of cutting planes
  • Fourteen different MIP feasibility heuristics, including advanced sub-MIP methods
  • Node presolve
  • Support for user cuts and lazy constraints
  • Non-disjoint subtree detection
  • Symmetry detection
  • Support for Special-Ordered Set constraints
  • Support for semi-continuous and semi-integer variables
  • Efficient Irreducible Infeasible Subsystem (IIS) detection
  • Feasibility relaxation feature for minimizing constraint violations for infeasible models
  • Solution pool provides access to multiple feasible solutions
  • Extensive callback capabilities
  • Support for MIP starts
Presolve
  • Six (6) major categories of reductions for LP models
  • Over fifteen (15) different categories of additional reductions for MIPs
  • Example reductions include: aggregation, bound strengthening, coefficient reduction, reduced-cost fixing, probing, and domination.
  • Automatic dualization of continuous models
  • Automatic linearization of quadratic objective and quadratic constraints

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