Monday, 16:15 - 16:40 h, Room: H 1028


Michel Baes
First-order methods for eigenvalue optimization

Coauthors: Michael Buergisser, Arkadi Nemirovski


Many semidefinite programming problems encountered in practice can be recast as minimizing the maximal eigenvalue of a convex combination of symmetric matrices. In this talk, we describe and analyze a series of first-order methods for solving this problem when the input matrices are large (of dimension 1000 to 10000 and more) and mildly sparse. We propose several accelerating strategies, notably in the step-size selection, and based on randomization, and illustrate the theoretical and practical efficiency of the new approach.


Talk 3 of the invited session Mon.3.H 1028
"Global rate guarantees in sparse optimization" [...]
Cluster 21
"Sparse optimization & compressed sensing" [...]


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