Adaptive Stochastic Mirror Descent for Constrained Optimization
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). First posted on arXiv 4 May 2017.
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