Gurobi solver for linear and mixed integer programming
About GurobiGurobi 4.0 is a state-of-the-art solver for linear programming (LP), quadratic programming (QP) and mixed-integer programming (MILP and MIQP). It was designed from the ground up to exploit modern multi-core processors. For solving LP and QP models, the Gurobi Optimizer includes high-performance implementations of the primal simplex method, the dual simplex method, and a parallel barrier solver. For MILP and MIQP models, the Gurobi Optimizer incorporates the latest methods including cutting planes and powerful solution heuristics. All models benefit from advanced presolve methods to simplify models and slash solve times.
Learn more about Gurobi optimization software
About OptimJ solver link for Gurobi
OptimJ™ for Gurobi lets you develop, debug and tune models in Java™ using state-of-the-art tools and techniques. It provides a clear and concise algebraic notation for optimization modeling, object-oriented programming for data modeling, and powerful bulk data manipulation primitives for pre- and post-processing.
OptimJ™ models are directly compatible with Java™ source code, existing Java libraires such as database access, Excel connection or graphical interfaces, leveraging existing code bases and training, and facilitating communication between optimization experts and IT teams.
OptimJ™ brings modern development tools such as Eclipse, CVS, JUnit or JavaDoc to optimization experts, improving productivity and quality.
You can try OptimJ™ solver link for Gurobi with a free 30-days evaluation licence including examples of OptimJ models for the Gurobi solver.
OptimJ™ solver link for Gurobi is reasonably priced, please contact us for the details.
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Customer Quotes
We're going to deploy OptimJ capabilities for our ongoing Java-based projects to close a gap between optimization engines and Java applications.
Andrey Torzhkov,
Research Scientist,
Siemens Corporate Research.
With OptimJ you get the expressiveness of OPL™ with the integrability and flexibility of Ilog Concert™ -- the best of both worlds.
Luc Mercier,
Phd student,
Brown University.
Integrating optimization projects in a Java environment becomes a breeze using the Eclipse IDE, shortening project development times up to 50%.
OptimJ made it easy to use results from different solvers and combine exact methods with metaheuristics coded in Java, for solving complex industrial problems.
Médéric Suon,
Industrial Engineer,
I used OptimJ to implement a model for production planning in a polystyrene factory.
Luc Mercier,
Phd student,
Brown University.
We've succesfully applied OptimJ to improve an existing software application developed in one of our past numerical optimization projects.
Andrey Torzhkov,
Research Scientist,
Siemens Corporate Research.
Using OptimJ enabled a rapid development and integration of optimization models in Java-based applications.
Médéric Suon,
Industrial Engineer,
