# Python Mixed Integer Linear Programming

Are there any Mixed Integer Linear Programming(MILP) solver for Python?

Can GLPK python solve MILP problem? I read that it can solve Mixed integer problem.

I am very new to linear programming problem. So i am rather confused and cant really differentiate if Mixed Integer Programming is different from Mixed Integer Linear programming(MILP).

**Pulp** is a python modeling interface that hooks up to solvers like **CBC**(open source), **CPLEX** (commercial), **Gurobi**(commercial), **XPRESS-MP**(commercial) and **YALMIP**(open source).

You can also use **Pyomo** to model the optimization problem and then call an external solver, namely CPLEX, Gurobim GLPK and the AMPL solver library.

You can also call GLPK from **GLPK/Python**, **PyGLPK** or **PyMathProg**.

Yet another modelling language is **CMPL**, which has a python interface for MIP solvers (for linear programs only).

All the above solvers solve Mixed Integer **Linear** Programs, while some of them (CPLEX, GUROBI and XRESS-MP for sure) can solve Mixed Integer **Quadratic** Programs and Quadratically constrained quadratic programs (and also conic programs but this probably goes beyond the scope of this question).

MIP refers to Mixed integer programs, but it is commonly used to refer to linear programs only. To make the terminology more precise, one should always refer to MILP or MINLP (Mixed integer non-linear programming).

Note that CPLEX and GUROBI have their own python APIs as well, but they (and also) XPRESS-MP are commercial products, but free for academic research. **CyLP** is similar to Pulp above but interfaces with the COIN-OR solvers CBC and CGL and CLP.

Note that there is a big difference in the performance of commercial and free solvers: the latter are falling behind the former by a large margin. **SCIP** is * perhaps the best non-commercial solver* (see below for an update). Its python interface, PySCIPOpt, is

**here**.

Also, have a look at this SO question.

Finally, if you are interested at a simple constraint solver (not optimization) then have a look at **python-constraint**.

I hope this helps!

**UPDATES**

More solvers and python interfaces that fell into my radar:

MIPCL, which appears to be ~~one of the fastest~~ the fastest non-commercial MIP solver, has a python interface that has quite good documentation. Note, however, that the Python API does not include the advanced functionality that comes together with the native MIPCLShell. I particularly like the MIPCL-PY manual, which demonstrates an array of models used in Operations Management, on top of some small-scale implementations. It is a very interesting introductory manual in its own right, regardless of which solver/API one may want to make use of.

Google Optimization Tools, which include a multitude of functionalities, such as

- A constraint programming solver and a linear programming (
*not MIP*) solver - An interface for MIP solvers (supports CBC, CLP, GLOP, GLPK, Gurobi, CPLEX, and SCIP)
- Specialized algorithms for graphs, for the Travelling Salesman Problem, the Vehicle Routing problem and for Bin packing & Knapsack problems

It has extensive documentation of several traditional OR problems and simple implementations. I could not find a complete Python API documentation, although there exist some examples here. It is somewhat unclear to me how other solvers hook up on the interface and whether methods of these solvers are available.

CVXOPT, an open-source package for convex optimization, which interfaces to GLPK (open source) and MOSEK (commercial). It is versatile, as it can tackle many problem classes (notably linear, second-order, semidefinite, convex nonlinear). The only disadvantage is that it modeling complex problems may be cumbersome, as the user needs to pass the data in a "Matlab-y" fashion (i.e., to specify the matrix, rhs vectors, etc). However, it can be called from the modeling interfaces PICOS and...

CVXPY, a python-embedded optimization language for convex optimization problems, which contains `CVXOPT`

as a default solver, but it can hook up to the usual MIP solvers.

Thanks to RedPanda for pointing out that `CVXOPT/CVXPY`

support MIP solvers as well.

For a very comprehensive article on optimization modeling capabilities of packages and object-oriented languages (not restricted to Python), check this article.

From: stackoverflow.com/q/26305704