Nonmonotone Proximal Gradient Method for Composite Multiobjective Optimization Problems

Abstract In this paper, we introduce proximal gradient methods with non-monotone Armijo line search rule of both kinds, max-type and average-type, to solve composite multiobjective optimization problems (CMOP). Moreover, the convergence analysis is given, and we show that all accumulation points of a sequence generated by both of the two kinds of nonmonotone proximal gradient methods are Pareto stationary points of (CMOP). Finally, we present numerical experiments illustrating the practical performance of these methods

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Publication Details

Journal
Journal of Optimization Theory and Applications
Published
2025-04-16
DOI
https://doi.org/10.1007/s10957-025-02667-8
Primary Topic
Advanced Multi-Objective Optimization Algorithms
Type
article
Field-Weighted Citation Impact
0.00

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article

Nonmonotone Proximal Gradient Method for Composite Multiobjective Optimization Problems

Elisabeth Köbis, Jianwen Peng, Hua Long Sun
Journal of Optimization Theory and Applications
Advanced Multi-Objective Optimization Algorithms
article

Nonmonotone Proximal Gradient Method for Composite Multiobjective Optimization Problems

Elisabeth Köbis, Jianwen Peng, Hua Long Sun
article en

Abstract

Abstract In this paper, we introduce proximal gradient methods with non-monotone Armijo line search rule of both kinds, max-type and average-type, to solve composite multiobjective optimization problems (CMOP). Moreover, the convergence analysis is given, and we show that all accumulation points of a sequence generated by both of the two kinds of nonmonotone proximal gradient methods are Pareto stationary points of (CMOP). Finally, we present numerical experiments illustrating the practical performance of these methods

Journal of Optimization Theory and ApplicationsVol. 205(3)
Chongqing Normal University (CN), Norwegian University of Science and Technology (NO)
Norges Teknisk-Naturvitenskapelige Universitet, National Natural Science Foundation of China, St. Olavs Hospital Universitetssykehuset i Trondheim
Openalex Percentile: Top 91%
Advanced Multi-Objective Optimization Algorithms
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