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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