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From OpenOpt

subjected to

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• OpenOpt QP example

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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	commercial full version free for education free 90-days trial with limitations up to 500 vars/cons	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

See also: QCQP, MIQP, MIQCQP, SOCP, SDP, NLP