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<title>OPTLAB</title> <title>OPTLAB</title>
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<body> <body>
<h1 class="short">OPTLAB</h1> <h1 class="short">OPTLAB</h1>
<h1>Nonlinear Optimization LABoratory</h1>
<div><span style="white-space:nowrap"><!-- <div id="banner">
--><img width="35%" src="pmsm_shapeTopo.png"></a><!-- <img src="pmsm_shapeTopo.png" alt="">
--><img width="35%" src="mbb.png"></a><!-- <img src="busbar.png" alt="">
--><img width="35%" src="busbar.png"></a><!-- <img src="mbb.png" alt="">
--></span></div> </div>
<h1>Large-Scale Nonlinear Constrained Optimization Library</h1>
<p>
OPTLAB (OPTimization LABoratory) is a framework for formulating and solving
large-scale, nonlinear constrained optimization problems in an efficient and
portable manner. The default algorithm provided by OPTLAB is the method of
moving asymptotes (MMA)<a href="#1"><sup>1</sup></a>, implemented for both
shared and distributed (MPI) memory architectures
using <a href="http://www.mcs.anl.gov/petsc">PETSc</a>.
</p>
<p> <p>
OPTLAB is a fully parallel package for solving large-scale and nonlinear constrained OPTLAB is Copyright (c) 2018, E. Kuci, C. Geuzaine and P. Duysinx, University
optimization problems in an efficient </a> and portable manner. of Li&egrave;ge.
The package is distributed as a Software Development Kit (SDK) callable
from C, C++, Python, and Julia.
</p> </p>
<h2>Quick start</h2> <h2>Quick start</h2>
<p>
OPTLAB is distributed as part of the ONELAB bundle to solve large scale finite
element shape or topology optimization
problems<a href="#2"><sup>2</sup></a>. To test OPTLAB:
</p>
<ol> <ol>
<li>Download the Software Development Kit (SDK) for <li>Dowload the <a href="http://onelab.info">ONELAB software bundle</a>
<a href="http://onelab.info/optlab/bin/optlab-1.0.0-Windows64.zip">Windows</a>, <li>Launch the app <img src="http://geuz.org/gmsh/gallery/icon.png" height=20px>
<a href="http://onelab.info/optlab/bin/optlab-1.0.0-Linux64.zip">Linux </a> or <li>Open <code>tutorials/optlab/shape.py</code>
<a href="http://onelab.info/optlab/bin/optlab-1.0.0-MacOSX.zip">MacOS </a>. <li>Press <code>Run</code>
<li>... then learn how to use it by exploring
<a href="https://gitlab.onelab.info/optlab/tutorials">the tutorials</a>
</ol> </ol>
<p>
This assumes that you have a working Python installation on your computer,
including the <code>numpy</code> package.
</p>
<h2>OPTLAB Software Development Kit</h2>
<p>
A Software Development Kit (SDK) is also available for integrating OPTLAB with
your own C, C++, Python or Julia code.
</p>
<p>
Download the SDK for
<a href="http://onelab.info/optlab/bin/optlab-1.0.0-Windows64.zip">Windows 64-bit</a>,
<a href="http://onelab.info/optlab/bin/optlab-1.0.0-Windows32.zip">Windows 32-bit</a>,
<a href="http://onelab.info/optlab/bin/optlab-1.0.0-Linux64.zip">Linux 64-bit</a>,
<a href="http://onelab.info/optlab/bin/optlab-1.0.0-Linux32.zip">Linux 32-bit</a> or
<a href="http://onelab.info/optlab/bin/optlab-1.0.0-MacOSX.zip">MacOS</a>.
</p>
<h2>References</h2>
<div class="small">
<ol class="small">
<li><a name="1"></a> K. Svanberg. <em>The method of moving asymptotes - a
new method for structural optimization</em>. International journal for
numerical methods in engineering, 24 (2):359–373, 1987.
<li><a name="2"></a>E. Kuci, F. Henrotte, P. Duysinx, and C. Geuzaine.
<em>Design sensitivity analysis for shape optimization based on the Lie
derivative</em>. Computer Methods in Applied Mechanics and Engineering 317
(2017), pp. 702 –722.
</ol>
</div>
<h2>Sponsors</h2>
<p>
OPTLAB development was funded in part by the Walloon Region under
<a href="https://recherche-technologie.wallonie.be/projets/index.html?IDD=22338">WBGreen
grant No 1217703 (FEDO)</a> and the <a href="http://www.belspo.be">the Belgian
Science Policy</a> under grant IAP P7/02.
</p>
<center style="margin-top:4ex;margin-bottom:4ex">
<a href="http://www.ulg.ac.be"><img src="http://onelab.info/logo_uliege.jpg" height="68px"></a>&nbsp;
<a href="http://www.wallonie.be"><img src="http://onelab.info/logo_rw.jpg" height="68px"></a>&nbsp;
<a href="http://www.belspo.be"><img src="http://onelab.info/logo_belspo.jpg" height="68px"></a>
</center>
</body> </body>
</html> </html>
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