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An optimal method for stochastic composite optimization. (English) Zbl 1273.90136
The stochastic composite optimization problem under consideration consists in minimizing the sum $$f+h$$ of two convex functions $$f,h$$ defined on a compact convex set $$X\subset \mathbb{R}^{n}$$, assuming that $$\nabla f$$ and $$h$$ are globally Lipschitz. The author proposes two subgradient-type methods, namely, a modified version of the mirror-descent SA method due to A. Nemirovski, A. Juditsky, G. Lan and A. Shapiro [SIAM J. Optim. 19, No. 4, 1574–1609 (2009; Zbl 1189.90109)], which substantially improves the rate of convergence, and an accelerated stochastic approximation method, which can achieve an optimal rate of convergence. He illustrates the advantages of the latter method over other existing methods by discussing its performance for a particular class of stochastic optimization problems.

##### MSC:
 90C15 Stochastic programming 90C25 Convex programming 62L20 Stochastic approximation
NESTA; SUTIL
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##### References:
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