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We propose a stochastic gradient framework for solving stochastic composite convex optimization problems with (possibly) infinite number of linear inclusion constraints that need to be satisfied almost surely.
Acceleration of stochastic approximation by averaging
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Linear convergence of first order methods for non-strongly convex optimization
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Randomized projection methods for convex feasibility problems: conditioning and convergence rates
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