Sufficient Descent Riemannian Conjugate Gradient Methods

Hiroyuki Sakai () and Hideaki Iiduka ()
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Hiroyuki Sakai: Meiji University
Hideaki Iiduka: Meiji University

Journal of Optimization Theory and Applications, 2021, vol. 190, issue 1, No 6, 130-150

Abstract: Abstract This paper considers sufficient descent Riemannian conjugate gradient methods with line search algorithms. We propose two kinds of sufficient descent nonlinear conjugate gradient method and prove that these methods satisfy the sufficient descent condition on Riemannian manifolds. One is a hybrid method combining a Fletcher–Reeves-type method with a Polak–Ribière–Polyak-type method, and the other is a Hager–Zhang-type method, both of which are generalizations of those used in Euclidean space. Moreover, we prove that the hybrid method has a global convergence property under the strong Wolfe conditions and the Hager–Zhang-type method has the sufficient descent property regardless of whether a line search is used or not. Further, we review two kinds of line search algorithm on Riemannian manifolds and numerically compare our generalized methods by solving several Riemannian optimization problems. The results show that the performance of the proposed hybrid methods greatly depends on the type of line search used. Meanwhile, the Hager–Zhang-type method has the fast convergence property regardless of the type of line search used.

Keywords: Riemannian conjugate gradient method; Sufficient descent condition; Strong Wolfe conditions; Line search algorithm; 65K05; 90C26; 57R35 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10957-021-01874-3

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