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Communication Dans Un Congrès Année : 2012

Randomized First-Order algorithms for bilinear saddle point problems and their applications to l1 minimization

Résumé

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.
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Dates et versions

hal-00981904 , version 1 (23-04-2014)

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  • HAL Id : hal-00981904 , version 1

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Anatoli B. Juditsky, Fatma Kilinc Karzan, Arkadii S. Nemirovski. Randomized First-Order algorithms for bilinear saddle point problems and their applications to l1 minimization. ISMP 2012 - 21th International Symposium on Mathematical Programming, Aug 2012, Berlin, Germany. ⟨hal-00981904⟩
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