Tuesday, 15:15 - 15:40 h, Room: H 1012


Arkadi Nemirovski
Randomized first-order algorithms for bilinear saddle point problems and their applications to l1 minimization

Coauthors: Anatoli Juditsky, Fatma Kilinc-Karzan


In this talk, we propose randomized first-order algorithms for solving bilinear saddle points problems. Our developments are motivated by the need for sublinear time algorithms to solve large-scale parametric bilinear saddle point problems where cheap online assessment of solution quality is crucial. We present the theoretical efficiency estimates of our algorithms and discuss a number of applications, primarily to the problems of l1 minimization arising in sparsity-oriented Signal Processing.
We demonstrate, both theoretically and by numerical examples, that when seeking for medium-accuracy
solutions of large-scale l1 minimization problems, our randomized algorithms outperform
significantly (and progressively as the sizes of the problems grow) the state-of-the-art
deterministic methods.


Talk 1 of the invited session Tue.3.H 1012
"Large-scale structured optimization" [...]
Cluster 17
"Nonsmooth optimization" [...]


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