Stochastic variance-reduced prox-linear algorithms for nonconvex composite optimization

DOI10.1007/s10107-021-01709-zOpenAlexW3201952969MaRDI QIDQ2089785

Lin Xiao, Junyu Zhang

Publication date: 24 October 2022

Published in: Mathematical Programming. Series A. Series B (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2004.04357

zbMATH Keywords

nonsmooth optimizationvariance reductionsample complexityprox-linear algorithmstochastic composite optimization

Mathematics Subject Classification ID

Analysis of algorithms and problem complexity (68Q25) Nonconvex programming, global optimization (90C26) Randomized algorithms (68W20)

Related Items (4)

Hybrid SGD algorithms to solve stochastic composite optimization problems with application in sparse portfolio selection problems ⋮ Stochastic composition optimization of functions without Lipschitz continuous gradient ⋮ On the complexity of a stochastic Levenberg-Marquardt method ⋮ Stochastic Gauss-Newton algorithm with STORM estimators for nonconvex composite optimization

Uses Software

• ProxSARAH

Cites Work

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