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Adaptive Stochastic Mirror Descent for Constrained Optimization

Research paper by Anastasia Bayandina

Indexed on: 04 May '17Published on: 04 May '17Published in: arXiv - Mathematics - Optimization and Control



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.