https://www.lrde.epita.fr/index.php?title=Courses/OCVX&feed=atom&action=history
Courses/OCVX - Revision history
2024-03-28T21:41:25Z
Revision history for this page on the wiki
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https://www.lrde.epita.fr/index.php?title=Courses/OCVX&diff=125051&oldid=prev
Carlinet at 09:11, 17 February 2020
2020-02-17T09:11:39Z
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Carlinet
https://www.lrde.epita.fr/index.php?title=Courses/OCVX&diff=125050&oldid=prev
Carlinet at 09:10, 17 February 2020
2020-02-17T09:10:20Z
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Carlinet
https://www.lrde.epita.fr/index.php?title=Courses/OCVX&diff=124962&oldid=prev
Carlinet: Created page with "{{Course |visible=Yes |title=Optimisation Convexe |acronym=OCVX |teacher=tochon |duration=36h |optional course=non |objectives=Présenter les outils mathématiques permettant..."
2020-02-04T17:36:21Z
<p>Created page with "{{Course |visible=Yes |title=Optimisation Convexe |acronym=OCVX |teacher=tochon |duration=36h |optional course=non |objectives=Présenter les outils mathématiques permettant..."</p>
<p><b>New page</b></p><div>{{Course<br />
|visible=Yes<br />
|title=Optimisation Convexe<br />
|acronym=OCVX<br />
|teacher=tochon<br />
|duration=36h<br />
|optional course=non<br />
|objectives=Présenter les outils mathématiques permettant la formulation des problèmes d’optimisation convexe, et les outils algorithmiques en permettant la résolution.<br />
<br />
Ce cours introduit les algorithmes permettant de résoudre les problèmes d’optimisation convexe. Nombreux problèmes en traitement du signal et des images peuvent se formuler comme la recherche de l’optimum d’une fonction convexe, d’où l’intérêt pratique de maîtriser les outils théoriques sous-jacents à la résolution de ces problèmes. Ce module est organisé en deux blocs. Dans un premier temps, il s’attachera à apporter une compréhension qualitative des problèmes d’optimisation convexe dans un cadre général. L’accent sera mis dans un second temps sur une compréhension théorique des méthodes itératives permettant la résolution des problèmes d’optimisation convexe.<br />
|content=* Définition générale d’un problème d’optimisation avec et sans contraintes, recherche de solutions.<br />
* Résolution par Lagrangien et problème dual, conditions de Karush-Kuhn-Tucker<br />
* Optimisation par descentes de gradient. Méthodes à pas optimal, gradient conjugué. Méthode de Newton.<br />
* Analyse de complexité sous condition de stricte convexité.<br />
* Optimisation par méthodes de points intérieurs.<br />
* Étude d’un cas pratique : séparateurs à vaste marge.<br />
}}</div>
Carlinet