QP
From OpenOpt
Quadratic Problems (QP)
- subjected to
- OpenOpt QP example
-
(since v. 0.50) FuncDesigner QP example
all these solvers listed below can handle only convex QPs, for non-convex local optimum try using converter to NLP, if global optimum is required - try using interalg.
QP solvers connected to OpenOpt:
| Solver | License | Made by | Info |
|---|---|---|---|
| cvxopt_qp | GPL3 | Lieven Vandenberghe, Joachim Dahl | requires CVXOPT installed, can handle sparse matrices |
| qlcp | MIT | Enzo Michelangeli and IT Vision Ltd | qlcp code is included into OO (requires ver >= 0.32). Currently it handles dense probs only and involves inversion of H matrix (no walk-around is implemented yet). |
| (since v 0.51) octave_qp | GPL | GNU Octave | requires GNU Octave and oct2py installed, cannot handle sparse matrices yet (passing them into Octave is unimplemented in latest oct2py 0.4.0 yet) |
| (since v 0.51) MATLAB quadprog | proprietary | Matworks Inc. | Read MATLAB entry for details |
| (since v. 0.33) cplex |
| IBM (after ILOG acquisition) | requires cplex and its Python API installed |
| converter to nlp | Dmitrey | Example: r = p.solve('nlp:ipopt', plot=1). Can handle x0. Recommended solvers (mb require installation, see NLP doc page): ipopt, algencan. For ralg reducing p.ftol and p.xtol sometimes is helpful | |
| Future plans: knitro |
See also: QCQP, MIQP, MIQCQP, SOCP, SDP, NLP, SparseMatrices
