lp_solve solver for linear and mixed integer programming
About lp_solve
lp_solve is a free mixed integer linear programming solver based on the revised simplex method and the Branch-and-bound method for the integers.
lp_solve solves pure linear, (mixed) integer/binary, semi-continuous and special ordered sets (SOS) models. Both the objective function and the constraints have this restriction. Via the Branch-and-bound algorithm, it can handle integer variables, semi-continuous variables and Special Ordered Sets.
lp_solve has no limit on model size and accepts standard both lp or mps input files, but even that can be extended. Note however that some large models could give lp_solve a hard time and will even fail to solve.
About OptimJ solver link for lp_solve
OptimJ™ is available for free with lp_solve and 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.
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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,
