Changes between Version 4 and Version 5 of OptimizationProblems

Timestamp:

04/20/07 04:42:57 (6 years ago)

Author:

jarrod.millman

Comment:

--

OptimizationProblems

@@ -7 +7 @@
 
 This project is based on:
- 1. existing m-code from my OpenOpt experience (see below)
+ 1. existing m-code from my [http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=13115&objectType=file OpenOpt] experience (see below)
  1. Fortran standalone routines from our optimization department
  1. lots of scipy solvers + connecting to other, already written.
 
-Now afaik essential free optimization environment is absent, but I strongly believe it's just a matter of time, like appearing Linux vs Unix was. And maybe from your decision will depend, will it be Python-based or Ruby- or caml- or fortress- or Java- or something else (by someone else). For example, I know that there are some efforts to make it in LispNYC.org project GSoC ideas, see for example
-http://lispnyc.org/wiki.clp?page=soc07-details/quad-opt
-'automatic placement of legends on function plots'
-while those some scipy optimization solvers can't yield graphic output at all.
-
-BTW there are usually some solvers for solving systems of non-linear equations (like MATLAB, scipy fsolve or my nonSmoothSolve) attached.
+= Background and motivation =
+Now afaik essential free optimization environment is absent, but I strongly believe it's just a matter of time, like appearing Linux vs Unix was.
 
 I already had some experience with my [http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=13115&objectType=file OpenOpt] for MATLAB/Octave, see the examples in directory !OpenOpt/Examples & some pictures generated automatically.
 
+= Goal =
 The environment will provide scaling, unified convenient text & graphics output, checking user-supplied analytical derivatives, parallel calculations (for example for numerical gradient/subgradient obtaining), easy comparison & similar to TOMLAB unified for all solvers call:
 {{{
@@ -32 +29 @@
 Also in future I intend to connect glpk, lpsolve, COIN-OR & other free solvers to the environment. I don't mean writing python-c connection once again, I mean simple unified call {{{prob=LP(...), r=prob.run(solver, <optional options>)}}} with some benchmarking elements. As lot solvers as it can be done will be Python-written, for to avoid problems with installation C- & Fortran-code & for to RAD ability.
 
+= Timeline =
+
+Two milestones are defined.  The first would be contributed to scipy.optimize and the second would be to scikits.optimize.  Note that scikits.optimize can require dependencies such as matplotlib or envisage, which is not allowed for scipy.optimize.
+
+
+== Milestone 1 ==
+
  * write ralg() & ShorEllipsoid() solvers (Unconstrained: ~1 week, constrained: +2-3 weeks)
  * write nonSmoothSolve() : ~ 1-2 weeks
@@ -40 +44 @@
  * writing or connecting of some already existing NLP UC or box-bounded solvers
  * write module for checking 1st derivatives provided by user (less than 1 week)
-
-= Timeline =
-
-Two milestones are defined.
-
-== Milestone 1 ==
-
 
 Due *9th July.*