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Communication Dans Un Congrès Année : 2014

Deterministic and stochastic first order algorithms of large-scale convex optimization

Résumé

Deterministic and stochastic first order algorithms of large-scale convex optimization Syllabus: First Order Methods of proximal type Nonsmooth Black Box setting: deterministic and stochastic Mirror Descent algorithm (MD). Convex-Concave Saddle Point problems via MD. Utilizing problem's structure: Mirror Prox algorithm (MP). Smooth/Bilinear Saddle Point reformulations of convex problems: calculus and examples. The Mirror Prox algorithm. Favorable geometry domains and good proximal setups. Conditional Gradient type First Order Methods for problems with difficult geometry: Convex problems with difficult geometry. Smooth minimization, norm-regularized smooth minimization. Nonsmooth minimization.
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Dates et versions

hal-00981877 , version 1 (23-04-2014)

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  • HAL Id : hal-00981877 , version 1

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Anatoli B. Juditsky, Arkadii S. Nemirovski. Deterministic and stochastic first order algorithms of large-scale convex optimization. YES Workshop Stochastic Optimization and Online Learning, Mar 2014, Eindhoven, Netherlands. ⟨hal-00981877⟩
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