I have a scalable quadratic programming problem which has around 50-1000 variables with linear constraints. I am trying to find an effective solver for this. The matlab qp solver can only solve to optimality for 100 variables for my problem. Will cplex be any better? Are there any other tools which I can use.
Asked
Active
Viewed 504 times
-1
-
How about GLPK? I am not sure if it does quadratic programming though. – Autonomous Nov 16 '15 at 08:25
2 Answers
1
CPLEX will definitely be better. Everything depends on the environment you are working in. If it's not a problem for you to write a C++ program you can try COIN-OR projects, in particular Couenne http://www.coin-or.org/projects/Couenne.xml for nonlinear MIP's and IPOPT http://www.coin-or.org/projects/Ipopt.xml; for Python lovers they have Coopr, DilPy, GrumPy and other projects. You can also try GAMS https://www.gams.com/, it's great for any optimization problem. It's not a solver, more like modeling system with a large number of built in commercial and open-source solvers. It's free as long as your problem has no more than 50 variables and 50 constraints (as far as I remember).
serge_k
- 1,772
- 2
- 15
- 21
0
You can try the Opti Toolbox: http://www.i2c2.aut.ac.nz/Wiki/OPTI/
It has an interface to different solvers which you can use in Matlab. (Precompiled mex files.) It can solve quadratic problems.
JaBe
- 664
- 1
- 8
- 27