Description:

Universal Python-written numerical optimization toolbox. Problems: NLP, LP, QP, SDP, SOCP, DFP(Non-linear Data Fit), NSP(nonsmooth), MILP, LSP, LLSP, MMP, GLP, MINLP etc. Connects to dozens of solvers (some are C- or Fortran-written).

Provides graphic output of convergence and some more numerical optimization "MUST HAVE" features.

Our another tool FuncDesigner allows to involve automatic differentiation + more convenient modelling of some optimization problems and SLEs (systems of linear equations, possibly sparse/overdetermined).

Changes to previous version:

http://openopt.org/Changelog

BibTeX Entry: Download

URL: Project Homepage

Supported Operating Systems: Linux, Macosx, Windows, Macos, Unix, Solaris

Data Formats: Mps

Tags: Python, Optimization

Archive: download here

Other available revisons

Version	Changelog	Date
0.29	http://openopt.org/Changelog	June 15, 2010, 21:52:40
0.28	http://openopt.org/Changelog	March 15, 2010, 19:12:17
0.27	http://openopt.org/Changelog	December 15, 2009, 21:58:05
0.25	http://openopt.org/Changelog	September 15, 2009, 16:59:12
0.24	http://openopt.org/Changelog	June 15, 2009, 17:10:04
0.23	You'd better see it here: http://openopt.org/Changelog or http://forum.openopt.org/viewtopic.php?id=58 New class SDP (solvers: CVXOPT and DSDP) New class SOCP (solvers: CVXOPT, in future CVXOPT authors intend to connect DSDP SOCP solver, then it will be connected to OO) New class DFP (Data Fit Problem, syntax similar to MATLAB lsqcurvefit) Some changes to NLP/NSP solver ralg Some more minor changes, code cleanup, bugfixes, doc entries updates Changes for named variables syntax: Check derivatives for oofun oolin constraints now are rendered into linear ones, provided all inputs of the oolin involved are oovar instances Contributors: Thanks to Stepan Hlushak for writing GLP solver de (based on differential evolution) Backward incompatibilities: if you provide derivatives for constraints, then for each constraint c_i or h_j: R^n -> R^s_k you should provide dc_i or dh_j with exactly same number of outputs, i.e. R^n -> R^(s_k, n), otherwise correct solution is not guaranteed (for named variables syntax you shouldn't care of the issue, each oofun has single function for obtaining output and no more than a single user-provided function for obtaining output derivatives).	March 15, 2009, 19:41:24
0.21	Initial Announcement on mloss.org.	December 25, 2007, 18:30:08

Comments

doug (on March 28, 2009, 12:51:07)

Excellent resource. Surprising reliability (stability & accuracy) for a library of this breadth.

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