Adaptive Stochastic Mirror Descent for Constrained Optimization

Published 4 May 2017 in math.OC | (1705.02031v1)

Abstract: Mirror Descent (MD) is a well-known method of solving non-smooth convex optimization problems. This paper analyzes the stochastic variant of MD with adaptive stepsizes. Its convergence on average is shown to be faster than with the fixed stepsizes and optimal in terms of lower bounds.

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Authors (1)

Anastasia Bayandina

Citations (5)

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