[Scilab-Dev] optim gc with bounds often fails

[Jean-Pierre Dussault]

 Hi,

I use regularly the optimization functions in scilab. Actually, as a Faculty professor in optimization, I often test prototype optimization algorithms which I implement in scilab and test them. For unconstrained or bound constrained optimization, I compare with the optim command and with L-BFGS-B which I interfaced to be used from scilab.

I also use Scilab in my courses. Unfortunately, the optim command with gc for bound constraints is useless. Therefore, I am forced to instruct the students to install and use L-BFGS-B. One important usage is the denoising/deblurring of images, high dimension bound constrained problems unsuitable for the qn method.

Here is a report in which I give some more details.
https://dl.dropboxusercontent.com/u/18380848/BenchMark.pdf
The concluding table is
qn
        unconstrained
        for small dimensions

gc
        unconstrained
        recommended

nd
        unconstrained
        for non differentiable problems

qn
        bounded
        for small dimensions

gc
        bounded
        avoid


In summary, the optim cg for bound constrained problems is useless as it fails to converge most of the time. Perhaps converting professionally the toolbox draft L-BFGS-B I produced would be a useful addition to scilab?

Thanks,

JPD



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