一个简要的求解器pythonAPI汇总

来自于https://stackoverflow.com/questions/26305704/python-mixed-integer-linear-programming

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, Gurobi 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.