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22<h1 class="short">con<u>v</u>eks</h1>
23
24<div id="banner">
25  <img src="team25.png" alt="">
26  <img src="wrench.png" alt="">
27  <img src="pmsm_shapeTopo.png" alt="">
28  <img src="mbb.png" alt="">
29  <img src="busbar.png" alt="">
30</div>
31
32<h1>Nonlinear Constrained Optimization Library</h1>
33
34<p>
35  con<u>v</u>eks is a framework for formulating and solving large-scale nonlinear
36  constrained optimization problems in an efficient and portable manner. The
37  default algorithm provided by con<u>v</u>eks is the method of moving asymptotes
38  (MMA)<a href="#1"><sup>1</sup></a>, implemented
39  using <a href="http://www.mcs.anl.gov/petsc">PETSc</a>.
40</p>
41<p>
42  con<u>v</u>eks is Copyright (c) 2018, E. Kuci, C. Geuzaine and P. Duysinx, <a href="https://www.uliege.be">University
43  of Li&egrave;ge</a>.
44</p>
45
46<h2>Quick start</h2>
47
48<p>
49  con<u>v</u>eks is distributed as part of the <a href="/">ONELAB</a> software
50  bundle to solve large scale finite element shape or topology optimization
51  problems, using both direct and adjoint
52  formulations<a href="#2"><sup>2</sup></a>. To test con<u>v</u>eks:
53</p>
54<ol>
55  <li>Download the <a href="/">ONELAB software bundle</a>
56  <li>Launch the app <img src="https://gmsh.info/gallery/icon.png" height=20px>
57  <li>Open <code>tutorials/conveks/Team25/<a href="https://gitlab.onelab.info/conveks/tutorials/blob/master/Team25/shape.py">shape.py</a></code>
58  or <code>tutorials/conveks/Lbracket/<a href="https://gitlab.onelab.info/conveks/tutorials/blob/master/Lbracket/top

58o.py">topo.py</a></code>
59  <li>Press <code>Run</code>
60</ol>
61<p>
62  This assumes that you have a working <a href="http://python.org">Python</a> installation on your computer,
63  including the <a href="http://numpy.org">NumPy</a> package.
64</p>
65
66<h2>con<u>v</u>eks Software Development Kit</h2>
67
68<p>
69  A Software Development Kit (SDK) is also available for integrating con<u>v</u>eks with
70  your own C, C++, Python or Julia code.
71</p>
72<p>
73  Download the con<u>v</u>eks SDK for
74  <a href="bin/conveks-git-Windows64.zip">Windows</a>,
75  <a href="bin/conveks-git-Linux64.zip">Linux</a> or
76  <a href="bin/conveks-git-MacOSX.zip">macOS</a>.
77</p>
78<p>
79  The con<u>v</u>eks source code is currently not publicly available: contact the
80  authors for further information.
81</p>
82
83<h2>References</h2>
84
85<div class="small">
86  <ol class="small">
87    <li><a name="1"></a> K. Svanberg. <em>The method of moving asymptotes - a
88      new method for structural optimization</em>. International journal for
89      numerical methods in engineering, 24 (2):359–373, 1987.
90    <li><a name="2"></a>E. Kuci, F. Henrotte, P. Duysinx, and C. Geuzaine.
91      <em><a href="https://people.montefiore.uliege.be/geuzaine/preprints/preprint_sensitivity_lie.pdf">Design
92      sensitivity analysis for shape optimization based on the Lie
93      derivative</a></em>. Computer Methods in Applied Mechanics and Engineering
94      317 (2017), pp. 702 –722.
95  </ol>
96</div>
97
98<h2>Sponsors</h2>
99
100<p>
101  con<u>v</u>eks development was funded in part by the Walloon Region under
102  <a href="https://recherche-technologie.wallonie.be/projets/index.html?IDD=22338">WBGreen
103grant No 1217703 (FEDO)</a> and the <a href="http://www.belspo.be">the Belgian
104Science Policy</a> under grant IAP P7/02.
105</p>
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