How Coliop Compares to Alternatives: A Quick Guide

7 Practical Uses for Coliop Today

1. Modeling linear and mixed-integer optimization problems

   • Use CMPL (Coliop Mathematical Programming Language) inside Coliop to express LP and MIP models in a math-like syntax, then solve with CBC, HiGHS, GLPK, Gurobi or CPLEX.

2. Rapid prototyping of optimization models

   • Write, edit and test models quickly in Coliop’s IDE (editor, syntax highlighting, examples) to iterate on formulations and constraints.

3. Teaching operations research and optimization

   • Use Coliop + CMPL in classroom exercises and assignments to demonstrate modelling concepts, solver behavior, and solution interpretation.

4. Integrating optimization into Python or Java workflows

   • Use pyCmpl or jCmpl APIs to define data, run CMPL models programmatically, and read solutions into application code for automated workflows.

5. Distributed or remote solving

   • Send models to a CMPLServer (XML-RPC) to run large or compute-intensive problems on a remote high-performance machine and retrieve results asynchronously.

6. Solver-agnostic benchmarking and comparison

   • Generate model instances (MPS/OSiL) from CMPL and run them with different solvers to compare performance, solution quality, or tuning of solver parameters.

7. Embedding optimization in spreadsheets and SolverStudio

   • Use the SolverStudio CMPL processor or export CMPL models to integrate optimization directly within Excel-based workflows for planners and analysts.