Free Optimization Projects
CMPL (Coliop|Coin Mathematical Programming Language) is a mathematical programming language and a system for optimization of linear problems (LP and MIP). CMPL can execute Gurobi directly to solve a generated model instance. CMPL is a COIN-OR open source project created by the Technical University of Applied Sciences Wildau and the Institute for Operations Research and Business Management at the Martin Luther University Halle-Wittenberg.
Coopr is a collection of Python software packages for formulating and analyzing a diverse set of optimization models. A key driver for Coopr development is Pyomo, a Coopr package for modeling optimization applications in Python. Pyomo can be used to define symbolic problems, create concrete problem instances, and apply optimizers such as Gurobi. The Gurobi solver class for Coopr was developed by Jean-Paul Watson.
Gurobi.jl is a Gurobi interface for Julia, a technical computing language that aims to combine MATLAB's productivity and C's performance. Gurobi.jl provides a set of Julia types and functions for constructing and solving LP, QP and MIP models with Gurobi. Gurobi.jl is developed by Dahua Lin.
Gurobi Mex is a MATLAB® interface for Gurobi. It enables MATLAB to solve linear and mixed-integer optimization problems using Gurobi. The Gurobi Mex interface is open source; its source code serves as a start point for those who want to develop a customized MATLAB interface for Gurobi. Gurobi Mex was developed by Wotao Yin.
Open Solver Interface (Osi) provides an abstract base class to a generic linear programming (LP) solver, along with derived classes for specific solvers. To interact with Gurobi, Osi provides the derived class OsiGrb. Osi is written in C++ and is released as open source code as part of the COIN-OR initiative. The Gurobi derived class was developed by Stefan Vigerske.
Optimization Zen provides Optimization.Framework, a system that lets you formulate mathematical models in C# and solve them using a variety of solvers, including the Gurobi Optimizer. Optimization.Framework was developed at the University of Paderborn.
PuLP is a modeling system written in Python. PuLP can generate MPS or LP files, and it can call Gurobi directly to solve LP and MIP models. PuLP is free and open source. The Gurobi solver class for PuLP was developed by Stuart Mitchell.
SolverStudio makes it easy to develop and deliver Gurobi optimization models using Excel. Create your model inside Excel using Python from within the SolverStudio editor window. This model then gets saved with the spreadsheet and 'compiled' and run whenever the user clicks Solve. Data entered into the spreadsheet is automatically available to the model. SolverStudio was developed by Andrew Mason.
YALMIP is a language for advanced modeling and solution of convex and nonconvex optimization problems. It is implemented as a free (as in no charge) toolbox for MATLAB. YALMIP focuses on the language and the higher level algorithms, while relying on external solvers such as Gurobi for computation. YALMIP was developed by Johan Lofberg.