Best open source Mixed Integer Optimization Solver [closed]

Answer 1

I recommend checking out the COIN project. COIN OR

Many good solvers here, including ipOPT for nonlinear problems and a couple mixed integer solvers as well.

Answer 2

I have used DICOPT using the NEOS server (http://www.neos-server.org/neos/solvers/minco:DICOPT/GAMS.html) to solve large (approx 1k variables and 1k constraints) mixed integer non-linear programs and found it excellent.

For my problem DICOPT did much better than the other MINLP solvers listed on the neos server BARON/KNITRO/LINDO/SBB etc.

There are certain constraints to submitting jobs to NEOS and it is slightly cumbersome but the free access to a powerful commercial solver more than makes up for it.

Answer 3

Try the SCIP solver. I have used it for MILP problems with over 300K variables with good performance. Its MILP performance is much better than GLPK. Gurobi has also excellent performance for MILP problems (and typically better than SCIP (May 2011)), but it might be costly if you are not an academic user. Gurobi will use multicores to speed up the solver.

Answer 4

Another endorsement for COIN-OR. We found that the linear optimiser component (Clp) was very strong, and the mixed integer component (Cbc) could be tuned quite well with some analysis. We compared with LP-Solve and GLPK.

For really tough problems, a commercial solver is the way to go.

Answer 5

100k variables is a large problem. Many of the open-source libraries do not function well with that many variables. From what I've read lp_solve has only been tested for around 30k variables. Using the commercial system may be your only choice.

Answer 6

I'll add the following to the already excellent answers.

Whilst, as others have pointed out, commercial solvers are much faster and more capable than the free alternatives it's important to consider how much of an optimality gap you can accept. For large problems with many integer variables you may get much faster solve-times if you can accept 1% or even greater optimality gap (defaults tend to be around 0.01% or less).

Of course if you are solving a problem where 1% translates into millions of dollars this is not acceptable - hence the market for top-notch solvers. (Some comparisons of Gurobi to free solvers here)

I would agree with others that using a platform that generates solver-independent problem files (such as *.mps, *.lp files) is worthwhile as you can then try out other solvers. I'm using PuLP and am finding it, and the free COIN_CBC solver, excellent. Although limited for problems with many integer variables.