Momentum-based variance-reduced proximal stochastic gradient method for composite nonconvex stochastic optimization

DOI10.1007/s10957-022-02132-wOpenAlexW4311114022MaRDI QIDQ2679567

Yibo Xu, Yang-yang Xu

Publication date: 23 January 2023

Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2006.00425

zbMATH Keywords

variance reductionmomentumstochastic gradient methodsmall-batch training

Mathematics Subject Classification ID

Analysis of algorithms and problem complexity (68Q25) Numerical mathematical programming methods (65K05) Nonconvex programming, global optimization (90C26) Stochastic programming (90C15)

Related Items (2)

Accelerated doubly stochastic gradient descent for tensor CP decomposition ⋮ Stochastic inexact augmented Lagrangian method for nonconvex expectation constrained optimization

Uses Software

• LIBSVM
• CIFAR
• ProxSARAH

Cites Work

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• Accelerated gradient methods for nonconvex nonlinear and stochastic programming
• A hybrid stochastic optimization framework for composite nonconvex optimization
• Stochastic subgradient method converges on tame functions
• Block Stochastic Gradient Iteration for Convex and Nonconvex Optimization
• Stochastic Model-Based Minimization of Weakly Convex Functions
• Online Nonnegative Matrix Factorization With Outliers
• Stochastic First- and Zeroth-Order Methods for Nonconvex Stochastic Programming
• A Stochastic Approximation Method
• Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization

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