QP
From OpenOpt
Quadratic Problems (QP)
- subjected to
- OpenOpt 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 a GLP solver on the obtained NLP
QP solvers connected to OpenOpt:
Solver | License | Made by | Info |
---|---|---|---|
cvxopt_qp | GPL3 | Lieven Vandenberghe, Joachim Dahl | requires CVXOPT installed |
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. 0.33) 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 |