1,214 research outputs found
Solving variational inequalities with Stochastic Mirror-Prox algorithm
In this paper we consider iterative methods for stochastic variational
inequalities (s.v.i.) with monotone operators. Our basic assumption is that the
operator possesses both smooth and nonsmooth components. Further, only noisy
observations of the problem data are available. We develop a novel Stochastic
Mirror-Prox (SMP) algorithm for solving s.v.i. and show that with the
convenient stepsize strategy it attains the optimal rates of convergence with
respect to the problem parameters. We apply the SMP algorithm to Stochastic
composite minimization and describe particular applications to Stochastic
Semidefinite Feasability problem and Eigenvalue minimization
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