PRP, HS and LS Conjugate Gradient Methods for Interval-Valued Multiobjective Optimization Problems

Fuente: arXiv
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Autori principali: Mondal, Tapas, Ghosh, Debdulal, Peng, Zai-Yun, Zhao, Yong
Natura: Preprint
Pubblicazione: 2026
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author Mondal, Tapas
Ghosh, Debdulal
Peng, Zai-Yun
Zhao, Yong
author_facet Mondal, Tapas
Ghosh, Debdulal
Peng, Zai-Yun
Zhao, Yong
contents In this article, we develop an efficient algorithm based on three special variants of the nonlinear conjugate gradient method, namely, the Polak--Ribiere--Polyak, Hestenes--Stiefel, and Liu--Story schemes for computing Pareto critical points in unconstrained interval-valued multiobjective optimization problems. The proposed algorithm incorporates a Wolfe line search strategy to determine a suitable range of step size that satisfies the standard Wolfe conditions. For each of the proposed variants of the nonlinear conjugate gradient method, we establish rigorous global convergence results under appropriate assumptions. To demonstrate the effectiveness of the proposed methods, we conduct numerical experiments on a set of benchmark test problems and present a comprehensive performance profile analysis.
format Preprint
id arxiv_https___arxiv_org_abs_2604_24605
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle PRP, HS and LS Conjugate Gradient Methods for Interval-Valued Multiobjective Optimization Problems
Mondal, Tapas
Ghosh, Debdulal
Peng, Zai-Yun
Zhao, Yong
Optimization and Control
In this article, we develop an efficient algorithm based on three special variants of the nonlinear conjugate gradient method, namely, the Polak--Ribiere--Polyak, Hestenes--Stiefel, and Liu--Story schemes for computing Pareto critical points in unconstrained interval-valued multiobjective optimization problems. The proposed algorithm incorporates a Wolfe line search strategy to determine a suitable range of step size that satisfies the standard Wolfe conditions. For each of the proposed variants of the nonlinear conjugate gradient method, we establish rigorous global convergence results under appropriate assumptions. To demonstrate the effectiveness of the proposed methods, we conduct numerical experiments on a set of benchmark test problems and present a comprehensive performance profile analysis.
title PRP, HS and LS Conjugate Gradient Methods for Interval-Valued Multiobjective Optimization Problems
topic Optimization and Control
url https://arxiv.org/abs/2604.24605