Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/112294
Title: Convergence Rates of the Stochastic Alternating Algorithm for Bi-Objective Optimization
Authors: Liu, Suyun
Vicente, Luís Filipe de Castro Nunes 
Keywords: Multi-objective optimization; Pareto front; Stochastic optimization; Alternating optimization
Issue Date: 2023
Publisher: Springer Nature
Project: UIDB/00324/2020 
Serial title, monograph or event: Journal of Optimization Theory and Applications
Volume: 198
Issue: 1
Abstract: Stochastic alternating algorithms for bi-objective optimization are considered when optimizing two conflicting functions for which optimization steps have to be applied separately for each function. Such algorithms consist of applying a certain number of steps of gradient or subgradient descent on each single objective at each iteration. In this paper, we show that stochastic alternating algorithms achieve a sublinear convergence rate of O(1/T ), under strong convexity, for the determination of a minimizer of a weighted-sum of the two functions, parameterized by the number of steps applied on each of them. An extension to the convex case is presented for which the rate weakens to O(1/ √ T ). These rates are valid also in the non-smooth case. Importantly, by varying the proportion of steps applied to each function, one can determine an approximation to the Pareto front.
URI: https://hdl.handle.net/10316/112294
ISSN: 0022-3239
1573-2878
DOI: 10.1007/s10957-023-02253-w
Rights: openAccess
Appears in Collections:I&D CMUC - Artigos em Revistas Internacionais
FCTUC Matemática - Artigos em Revistas Internacionais

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