Random Gradient Extrapolation for Distributed and Stochastic Optimization

DOI10.1137/17M1157891zbMath1401.90156arXiv1711.05762OpenAlexW2963373496WikidataQ129143221 ScholiaQ129143221MaRDI QIDQ4687240

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Publication date: 11 October 2018

Published in: SIAM Journal on Optimization (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1711.05762

zbMATH Keywords

stochastic optimizationrandomized methodfinite-sum optimizationdistributed machine learninggradient extrapolation

Mathematics Subject Classification ID

Semidefinite programming (90C22) Convex programming (90C25) Large-scale problems in mathematical programming (90C06) Numerical methods based on nonlinear programming (49M37)

Related Items (10)

On the Convergence of Stochastic Primal-Dual Hybrid Gradient ⋮ Numerical methods for the resource allocation problem in a computer network ⋮ Simple and Optimal Methods for Stochastic Variational Inequalities, I: Operator Extrapolation ⋮ Proximal stochastic recursive momentum algorithm for nonsmooth nonconvex optimization problems ⋮ A two-level distributed algorithm for nonconvex constrained optimization ⋮ Recent theoretical advances in decentralized distributed convex optimization ⋮ Decentralized and parallel primal and dual accelerated methods for stochastic convex programming problems ⋮ A randomized incremental primal-dual method for decentralized consensus optimization ⋮ Unnamed Item ⋮ Inexact model: a framework for optimization and variational inequalities

Uses Software

• Saga

Cites Work

• Unnamed Item
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