Stochastic ISTA/FISTA Adaptive Step Search Algorithms for Convex Composite Optimization

Fuente: arXiv
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Main Authors: Nguyen, Lam M., Scheinberg, Katya, Tran, Trang H.
Format: Preprint
Published: 2024
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author Nguyen, Lam M.
Scheinberg, Katya
Tran, Trang H.
author_facet Nguyen, Lam M.
Scheinberg, Katya
Tran, Trang H.
contents We develop and analyze stochastic variants of ISTA and a full backtracking FISTA algorithms [Beck and Teboulle, 2009, Scheinberg et al., 2014] for composite optimization without the assumption that stochastic gradient is an unbiased estimator. This work extends analysis of inexact fixed step ISTA/FISTA in [Schmidt et al., 2011] to the case of stochastic gradient estimates and adaptive step-size parameter chosen by backtracking. It also extends the framework for analyzing stochastic line-search method in [Cartis and Scheinberg, 2018] to the proximal gradient framework as well as to the accelerated first order methods.
format Preprint
id arxiv_https___arxiv_org_abs_2402_15646
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Stochastic ISTA/FISTA Adaptive Step Search Algorithms for Convex Composite Optimization
Nguyen, Lam M.
Scheinberg, Katya
Tran, Trang H.
Optimization and Control
We develop and analyze stochastic variants of ISTA and a full backtracking FISTA algorithms [Beck and Teboulle, 2009, Scheinberg et al., 2014] for composite optimization without the assumption that stochastic gradient is an unbiased estimator. This work extends analysis of inexact fixed step ISTA/FISTA in [Schmidt et al., 2011] to the case of stochastic gradient estimates and adaptive step-size parameter chosen by backtracking. It also extends the framework for analyzing stochastic line-search method in [Cartis and Scheinberg, 2018] to the proximal gradient framework as well as to the accelerated first order methods.
title Stochastic ISTA/FISTA Adaptive Step Search Algorithms for Convex Composite Optimization
topic Optimization and Control
url https://arxiv.org/abs/2402.15646