Algorithms of inertial mirror descent in convex problems of stochastic optimization

DOI10.1134/S0005117918010071zbMath1391.93298arXiv1705.01073OpenAlexW2962948066MaRDI QIDQ1641948

Alexander Nazin

Publication date: 20 June 2018

Published in: Automation and Remote Control (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1705.01073

zbMATH Keywords

convex optimizationmirror descentstochastic optimization problemheavy ball methodinertial mirror descent

Mathematics Subject Classification ID

Stochastic programming (90C15) Optimal stochastic control (93E20)

Related Items (4)

Elements of randomized forecasting and its application to daily electrical load prediction in a regional power system ⋮ Unifying mirror descent and dual averaging ⋮ Distributed Stochastic Inertial-Accelerated Methods with Delayed Derivatives for Nonconvex Problems ⋮ Tridimensional autonomous motion robust control of submersible ship based on averaged sub-gradient integral sliding mode approach

Uses Software

• ElemStatLearn

Cites Work

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• Recursive aggregation of estimators by the mirror descent algorithm with averaging
• The Ordered Subsets Mirror Descent Optimization Method with Applications to Tomography
• A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights
• One class of methods of unconditional minimization of a convex function, having a high rate of convergence
• Variational Analysis
• Some methods of speeding up the convergence of iteration methods
• Convex Analysis
• The elements of statistical learning. Data mining, inference, and prediction

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