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Quadratic Problems (QP)
 \mathbf{\frac{1}{2} x^T Hx + f^T x \rightarrow min}
subjected to
\mathbf{lb \le x \le ub}
\mathbf{A x \le b}
\mathbf{A_{eq} x = b_{eq}}

Attention 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)
  • 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
Future plans: knitro

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

Retrieved from "http://openopt.org/QP"
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