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Hi,<br>
<br>
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.<br>
<br>
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.<br>
<br>
Here is a report in which I give some more details.<br>
<blockquote><a class="moz-txt-link-freetext" href="https://dl.dropboxusercontent.com/u/18380848/BenchMark.pdf">https://dl.dropboxusercontent.com/u/18380848/BenchMark.pdf</a><br>
</blockquote>
The concluding table is<br>
<blockquote>
<table border="1" cellpadding="2" cellspacing="2" width="80%">
<tbody>
<tr>
<td valign="top">qn<br>
</td>
<td valign="top">unconstrained<br>
</td>
<td valign="top">for small dimensions<br>
</td>
</tr>
<tr>
<td valign="top">gc<br>
</td>
<td valign="top">unconstrained<br>
</td>
<td valign="top">recommended<br>
</td>
</tr>
<tr>
<td valign="top">nd<br>
</td>
<td valign="top">unconstrained<br>
</td>
<td valign="top">for non differentiable problems<br>
</td>
</tr>
<tr>
<td valign="top">qn<br>
</td>
<td valign="top">bounded<br>
</td>
<td valign="top">for small dimensions<br>
</td>
</tr>
<tr>
<td valign="top">gc<br>
</td>
<td valign="top">bounded<br>
</td>
<td valign="top">avoid<br>
</td>
</tr>
</tbody>
</table>
<br>
</blockquote>
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?
<br>
<br>
Thanks,<br>
<br>
JPD<br>
<br>
<br>
<br>
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