Work

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

Year: 2026

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2604.24605

Authors: Mondal, Tapas, Ghosh, Debdulal, Peng, Zai-Yun, Zhao, Yong

Institutions: Indian Institute of Technology BHU, Banaras Hindu University, SRM Institute of Science and Technology, Yunnan Normal University, Chongqing Jiaotong University

Topic: Advanced Optimization Algorithms Research

Subfield: Numerical Analysis

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: Arthritis National Research Foundation, National Natural Science Foundation of China, Natural Science Foundation of Chongqing

Awards: G4092563515, G4292976619, G504305494, G5247007129, G6892761941

OpenAlex ID: W7156621170

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