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OpenOpt Changelog

0.29 (2010-June-15)

OpenOpt:

  • Some bugfixes
  • Some improvements for handling sparse matrices

FuncDesigner:

  • Some improvements for automatic differentiation
  • New feature: attached constraints
  • New feature: oosystem
  • Now you can model & solve ODE systems

ralg:

  • Bugfix for problems with nonlinear equality constraints
  • Major changes for problems with nConstraints>1

DerApproximator:

  • Function get_d2
  • Add new stencil

Backward incompatibilities:

  • By default stencil for NSP and used-created oofuns (e.g. connected from non-Python code) now have derivatives approximation stencil №3 instead of №2, it may lead to speed decrease (but you can set it to 2 or 1 manually).


0.28 (2010-March-15)

  • Some bugfixes, mostly related to scipy.sparse matrices used since previous release
  • Export FuncDesigner and OpenOpt LP / MILP to MPS format files

OpenOpt 0.28:

  • New solvers scipy.optimize fmin as scipy_fmin (NLP/NSP), lsqr (LLSP)
  • Major changes for ralg (one of changes - now all active constraints are taken together)

FuncDesigner 0.18:

DerApproximator 0.18:

  • minor code clean up

0.27 (2009-December-15)

OpenOpt 0.27:

  • scipy_slsqp solver - bugfix for problems where some box-bound constraints lb<= x <= ub are finite and some infinite (Python inf was incorrectly passed to Fortran code, so +/- inf has been replace by +/- 1e50)
  • ralg solver - bugfix for problems with several linear equality constraints (>1), and some minor changes
  • Now for some problems you can use p.minimize(), p.maximize() instead of p.solve()
  • Some code clean up and minor changes

FuncDesigner 0.17:

  • Bugfix for FuncDesigner functions sum, sin
  • Now you can use oovar/oofun indexation a[ind1:ind2] (yet you cannot use a[ind:] or a[:ind])
  • Add FuncDesigner example for system of linear equations, possibly overdetermined
  • Example of passing oovars through ordinary Python funcs
  • Add "required size" for FuncDesigner oovars
  • Now there are some more ways to extract optimal point coordinates from OpenOpt result structure, e.g. a_opt, b_opt, c_opt = r('a', 'b', 'c') or r(a, b, c).
  • If you have scipy installed, it yields some benefits to automatic differentiation for large-scale problems

DerApproximator 0.17:

  • some minor fixes for some functions output data formats

0.25 (2009-September-15)

Backward incompatibilities:

  • oovar-oofun have been moved to standalone package (FuncDesigner)
  • For some problems with nonlinear funcs, where user-supplied derivatives are absent, you should have DerApproximator installed.


0.24 (2009-June-15)

  • New class: MINLP - mixed-integer nonlinear problems
  • New class: Linear Uniform Norm Problems (LUNP)
  • Now DFP can handle user-supplied derivatives
  • Some changes for finite-difference derivatives approximation
  • New handling of linear equality constraints by ralg and some other changes for the solver
  • Some important bugfixes, code cleanup, minor doc improvements

Backward incompatibilities:

  • Connection to PSwarm 1.4 instead of 1.3 (Python API is backward-incompatible for 1.4)
  • Default p.storeIterPoints set to False (it affects r.iterValues.x - for large scale problems it eats quite lot of memory)


0.23 (2009-March-15)

  • 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:

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).


0.21 (2008-December-15)

  • major changes for NLP / NSP solver ralg
  • new converter: qp2nlp
  • new converter: lp2nlp
  • new converter: nllsp2nlp
  • new converter: nlsp2nlp
  • new converter: minimax to NLP
  • Introducing OpenOpt GUI
  • PSwarm 1.3 has been connected instead of v 1.1. It has much more parameters to modify and ability to use user-supplied callback function.
  • Lots of changes related to oofun: add oovar and oolin, now fixed oovars can be correctly handled
  • enhanced iterfcn connection for scipy fmin_cobyla
  • some doc updates for result structure
  • new openopt API func: oosolver
  • some code cleanup and bugfixes
  • some other minor changes


Backward incompatibilities:

  • instead of "from scikits.openopt import ..." now you should use "from openopt import ..."
  • LSP has been renamed to NLLSP (Non-Linear Least Squares Problem)
  • for oofuns support of ordinary variables (x) had been ceased (it's hard to keep maintaining), use oovars instead.


0.19 (2008-September-15)

  • Some changes for NLP/NSP solver ralg (especially related to handling linear constraints Ax <= b, Aeq x = beq, lb <= x <= ub)
  • Bugfix for ralg IPOPT linear constraints handling
  • ALGENCAN v 2.0.x has been connected (v 1.0 is no longer supported, v 2.0.3 or later is required)
  • bugfix for constrained NLSP graphic output (constrained nssolve isn't turned to latest ralg version yet)
  • scale parameter for lpSolve (p.scale = {False} | True | 0 | 1)
  • New OO class LLAVP (linear least absolute values aka linear least deviations)
  • Improved handling of non-linear functions with restricted dom
  • GLP (global) solver galileo now can handle integer problems (via p.useInteger = 1 or True)
  • Another one GLP solver connected: pswarm
  • Lots of work related to oofun concept (see OO Doc page for details)
  • Add converters llsp2nlp, llavp2nsp
  • Convenient handling of maximization problems (via p.goal = 'max' or 'maximum')
  • Some code clean up and bugfixes


Backward incompatibilities:

  • Changed objective function in LLSP
  • MATLAB-style gradtol renamed to gtol (for to provide same syntax to scipy.optimize fmin_bfgs, fmin_cg and less-to-type)


0.18 (2008-June-15)

  • connection to glpk MILP solver (requires cvxopt v >= 1.0)
  • connection to NLP solver IPOPT (requires pyipopt installation, that is currently available for Linux only)
  • major changes for ralg
  • splitting non-linear constraints can benefit for some solvers
  • unified text output for NLP solvers
  • handling of maximization problems (via p.goal = 'max' or 'maximum')
  • some bugfixes, lots of code cleanup


0.17 (2008-March-15)

  • new classes: GLP (global problem), MMP (mini-max problem)
  • several new solvers written: goldenSection, nsmm
  • some more solvers connected: scipy_slsqp, bvls, galileo
  • possibility to change default solver parameters
  • user-defined callback functions
  • changes in auto derivatives check
  • "noise" parameter for noisy functions
  • some changes to NLP/NSP solver ralg
  • some changes in graphical output: initial estimations xlim, ylim
  • scaling
  • some bugfixes


0.15 (2007-December-15)

  • some new classes
  • several new solvers written
  • some more solvers connected
  • NLP/NSP solver ralg can handle constrained problems
  • some bugfixes
  • some enhancements in graphical output (especially for constrained problems)
Retrieved from "http://openopt.org/Changelog"
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