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

We consider the problem of minimizing composite functions of the form f ( g ( x ) ) + h ( x ) , where f and h are convex functions (which can be nonsmooth) and g is a smooth vector mapping. In addition, we assume that g is the average of finite number of component mappings or the expectation over...

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Bibliographic Details
Published in:	Mathematical programming Vol. 195; no. 1-2; pp. 649 - 691
Main Authors:	Zhang, Junyu, Xiao, Lin
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2022 Springer Springer Nature B.V
Subjects:	Algorithms Calculus of Variations and Optimal Control; Optimization Combinatorics Composite functions Full Length Paper Jacobians Mapping Mathematical and Computational Physics Mathematical Methods in Physics Mathematics Mathematics and Statistics Mathematics of Computing Numerical Analysis Optimization Theoretical Variance 68W20 68Q25 Variance reduction Stochastic composite optimization Nonsmooth optimization 90C26 Sample complexity Prox-linear algorithm
ISSN:	0025-5610, 1436-4646
Online Access:	Get full text
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