Develops truncated-gradient mirror descent algorithms for robust convex stochastic optimization and establishes sub-Gaussian confidence bounds under weak noise tail assumptions in convex and strongly convex cases.
and Yudin, D.B., Slozhnost’ zadach i effektivnost’ metodov optimizatsii , Moscow: Nauka, 1979
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Algorithms of Robust Stochastic Optimization Based on Mirror Descent Method
Develops truncated-gradient mirror descent algorithms for robust convex stochastic optimization and establishes sub-Gaussian confidence bounds under weak noise tail assumptions in convex and strongly convex cases.