Changes between Version 7 and Version 8 of OptimizationProblems

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Timestamp:
04/20/07 04:52:37 (6 years ago)
Author:
jarrod.millman
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  • OptimizationProblems

    v7 v8  
    2424}}} 
    2525 
    26 output structure r will include algorithm used, license of the solver used, its authors, web homepage of the solver, time, cputime elapsed, and much more. Some of the info will be printed (by default) before or after solver's work. 
    27 All of the above is already done in my OpenOpt version for MATLAB/Octave (parallel - currently only for objfun gradient obtaining, not constraints) but for many reasons (the main is pass-by-copy in MATLAB/Octave vs pass-by-reference in Python) I'm rewriting all the code to Python now & intend to continue development using Python language. GSoC support would helped me very much. 
     26output structure r will include algorithm used, license of the solver used, its authors, web homepage of the solver, time, cputime elapsed, and much more. Some of the info will be printed (by default) before or after solver's work.  All of the above is already done in my [http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=13115&objectType=file OpenOpt] version for MATLAB/Octave (parallel - currently only for objfun gradient obtaining, not constraints) but for many reasons (the main is pass-by-copy in MATLAB/Octave vs pass-by-reference in Python) I'm rewriting all the code to Python now & intend to continue development using Python language. 
     27 
     28 * write ralg() & ShorEllipsoid() solvers (Unconstrained: ~1 week, constrained: +2-3 weeks) 
     29 * write nonSmoothSolve() : ~ 1-2 weeks 
     30 * writing MATLAB bintprog equivalent (f*x->min, A*x<=b, Aeq*x=beq) based on rd.  Shilo (& others) 
     31 * write a Python version of GRASP: ~2-3 weeks 
     32 * create an optimization environment for Python that is similar to MATLAB/Octave (1-1.5 months) 
     33 * write MATLAB fmincon equivalent (smooth constrained optimization, c(x)<=0, h(x)=0, linear constraints +1st & 2nd derivatives) based on Nikitin & Pshenichniy 
     34 * writing or connecting of some already existing NLP UC or box-bounded solvers 
     35 * write module for checking 1st derivatives provided by user (less than 1 week) 
    2836 
    2937Also 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. 
    … …  
    3543 
    3644== Milestone 1 == 
    37  
    38  * write ralg() & ShorEllipsoid() solvers (Unconstrained: ~1 week, constrained: +2-3 weeks) 
    39  * write nonSmoothSolve() : ~ 1-2 weeks 
    40  * writing MATLAB bintprog equivalent (f*x->min, A*x<=b, Aeq*x=beq) based on rd.  Shilo (& others) 
    41  * write a Python version of GRASP: ~2-3 weeks 
    42  * create an optimization environment for Python that is similar to MATLAB/Octave (1-1.5 months) 
    43  * write MATLAB fmincon equivalent (smooth constrained optimization, c(x)<=0, h(x)=0, linear constraints +1st & 2nd derivatives) based on Nikitin & Pshenichniy 
    44  * writing or connecting of some already existing NLP UC or box-bounded solvers 
    45  * write module for checking 1st derivatives provided by user (less than 1 week) 
    4645 
    4746=== Getting Started (2 weeks) ===