PRP, HS and LS Conjugate Gradient Methods for Interval-Valued Multiobjective Optimization Problems
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908995570106368 |
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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 |