Changes between Version 1 and Version 2 of OptimizationProblems

Timestamp:

04/20/07 04:25:38 (6 years ago)

Author:

jarrod.millman

Comment:

--

OptimizationProblems

@@ -1 @@
-There are lots of commercial modelling systems for numerical optimization like AMPL(www.ampl.com), GAMS(www.gams.com), TOMLAB are. Just see for example
-http://tomopt.com/tomlab/company/customers.php – tomlab users
-http://tomopt.com/tomlab/products/prices/commercial.php – tomlab prices
+As far as I understood there are very few constrained solvers for Python. In google I failed to find anything but the CVXOPT, which consists mostly of wrappers to commercial mosek; some LP/MIP wrappers to GNU C- or f- code; and some optimization routines from scipy, of course. 
 
-My proposition is creating a free Python-based equivalent to them, + connecting some non-smooth & network solvers that our optimization department researches, + inviting our collaborators from other optimization departments of our & some other institutes to provide their own solvers.
+There are lots of commercial modeling systems for numerical optimization like [http://www.ampl.com AMPL], [http://www.gams.com GAMS], [http://tomopt.com TOMLAB].  I propose to create a free Python-based equivalent to them, + connecting some non-smooth & network solvers that our optimization department researches, + inviting our collaborators from other optimization departments of our & some other institutes to provide their own solvers.
 
 This project is based on:
  1. existing m-code from my OpenOpt experience (see below)
- 1. fortran standalone routines from our optimization department
+ 1. Fortran standalone routines from our optimization department
  1. lots of scipy solvers + connecting to other, already written.
 
@@ -17 +15 @@
 BTW there are usually some solvers for solving systems of non-linear equations (like MATLAB, scipy fsolve or my nonSmoothSolve) attached.
 
-I already had some experience with my OpenOpt for MATLAB/Octave, see http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=13115&objectType=file
-there are some examples in directory OpenOpt/Examples & some pictures generated automatically. See http://www.box.net/shared/3jmi2hj8lk for my CV and detailed proposition.
+I already had some experience with my [http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=13115&objectType=file OpenOpt] for MATLAB/Octave, see http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=13115&objectType=file
+there are some examples in directory !OpenOpt/Examples & some pictures generated automatically. See http://www.box.net/shared/3jmi2hj8lk for my CV and detailed proposition.
 
 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:
@@ -29 +27 @@
 
 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.
+
+ * write ralg() & ShorEllipsoid() solvers
+(Unconstrained: ~1 week, constrained: +2-3 weeks)
+ * write nonSmoothSolve() : ~ 1-2 weeks
+ * writing MATLAB bintprog equivalent
+(f*x->min, A*x<=b, Aeq*x=beq)
+based on rd.  Shilo (& others)
+ * write a Python version of GRASP: ~2-3 weeks
+ * create an optimization environment for Python that is
+similar to MATLAB/Octave (1-1.5 months)
+ * write MATLAB fmincon equivalent (smooth constrained
+optimization, c(x)<=0, h(x)=0, linear constraints +1st
+& 2nd derivatives) based on Nikitin & Pshenichniy
+ * 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)
+