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, MOP 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, uncertainty and interval analysis, categorical variables, general logical constraints, more convenient modeling of some optimization problems, systems of (non)linear equations (solver "interalg" can find ALL roots), possibly sparse/overdetermined, systems of ordinary differential equations and much more.
- Changes to previous version:
Other available revisons
Version Changelog Date 0.45
March 15, 2013, 14:27:12 0.43
December 15, 2012, 16:06:18 0.42
September 15, 2012, 13:18:20 0.39
June 15, 2012, 12:09:14 0.38
March 15, 2012, 11:20:29 0.37
December 15, 2011, 17:47:08 0.36
September 15, 2011, 18:23:00 0.34
June 16, 2011, 13:28:12 0.33
March 16, 2011, 10:55:25 0.32
December 15, 2010, 15:37:24 0.31
September 15, 2010, 11:14:46 0.29
June 15, 2010, 21:52:40 0.28
March 15, 2010, 19:12:17 0.27
December 15, 2009, 21:58:05 0.25
September 15, 2009, 16:59:12 0.24
June 15, 2009, 17:10:04 0.23
You'd better see it here:
- 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
- Thanks to Stepan Hlushak for writing GLP solver de (based on differential evolution)
- 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
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