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 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.

You are welcome to our recently created Numerical Optimization Forum

http://forum.openopt.org

and the poll "What do you miss in OpenOpt framework?"

http://www.doodle.com/participation.html?pollId=a78g5mk9sf7dnrbe

Changes to previous version:

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).

BibTeX Entry: Download

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

Data Formats: None

Tags: Python, Optimization

Archive: download here

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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