Nonlinear conjugate gradient method for vector optimization on Riemannian manifolds with retraction and vector transport

Kangming Chen, Ellen Hidemi Fukuda and Hiroyuki Sato

Applied Mathematics and Computation, 2025, vol. 486, issue C

Abstract: In this paper, we propose nonlinear conjugate gradient methods for vector optimization on Riemannian manifolds. The concepts of Wolfe and Zoutendjik conditions are extended to Riemannian manifolds. Specifically, the existence of intervals of step sizes that satisfy the Wolfe conditions is established. The convergence analysis covers the vector extensions of the Fletcher–Reeves, conjugate descent, and Dai–Yuan parameters. Under some assumptions, we prove that the sequence obtained by the proposed algorithm can converge to a Pareto stationary point. Moreover, several other choices of the parameter are discussed. Numerical experiments illustrating the practical behavior of the methods are presented.

Keywords: Conjugate gradient method; Vector optimization; Riemannian manifolds; Wolfe conditions; Line search algorithm (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:486:y:2025:i:c:s0096300324004624

DOI: 10.1016/j.amc.2024.129001

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