Lecture

Semidefinite Programming: Formulations and Applications

In course

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Description

This lecture covers the standard form of Semidefinite Programming (SDP) formulations, including trace-constrained SDPs and their broad interest. It also explores SDP relaxations for non-convex problems, such as the maximum-weight cut of a graph, clustering with minimal sum-of-squares, and approximating the Lipschitz constant of neural networks. Additionally, it discusses optimization strategies like CGM with quadratic penalty, Augmented Lagrangian CGM, and their convergence guarantees.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_62lmz500

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Ontological neighbourhood

Mathematics

Discrete mathematics: Graph theory

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