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Python MIP (Mixed-Integer Linear Programming) Tools

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Python MIP is a collection of Python tools for the modeling and solution of Mixed-Integer Linear programs (MIPs). MIP syntax was inspired by Pulp. Just like CyLP it also provides access to advanced solver features like cut generation, lazy constraints, MIPstarts and solution Pools. Porting Pulp and Gurobi models should be quite easy.

Some of the main features of MIP are:

  • high level modeling: write your MIP models in Python as easily as in high level languages such as MathProg: operator overloading makes it easy to write linear expressions in Python;

  • full featured:

    • cut generators and lazy constraints: work with strong formulations with a large number of constraints by generating only the required inequalities during the branch and cut search;
    • solution pool: query the elite set of solutions found during the search;
    • MIPStart: use a problem dependent heuristic to generate initial feasible solutions for the MIP search.
  • fast: the Python MIP package calls directly the native dynamic loadable library of the installed solver using the modern python CFFI module; models are efficiently stored and optimized by the solver and MIP transparently handles all communication with your Python code; it is also compatible with the Pypy just in time compiler, meaning that you can have a much better performance, up to 25 times faster for the creation of large MIPs, than the official Gurobi python interface which only runs on CPython;

  • multi solver: Python MIP was written to be deeply integrated with the C libraries of the open-source COIN-OR Branch-&-Cut CBC solver and the commercial solver Gurobi; all details of communicating with different solvers are handled by Python-MIP and you write only one solver independent code;

  • written in modern typed Python 3 (requires Python 3.8 or newer).


Many Python-MIP examples are documented at

The code of these examples and additional ones (published in tutorials) can be downloaded at


The full Python-MIP documentation is available at

A PDF version is also available:

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Questions, suggestions and feature request can be posted at Discussions.

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