A limited-memory Riemannian symmetric rank-one trust-region method with an efficient algorithm for its subproblem
Authors
Wen Huang*, Kyle A. Gallivan
Abstract
Limited-memory versions of quasi-Newton methods are efficient methods for largescale optimization problems in the Euclidean space. In particular, a quasi-Newton symmetric rank-one update used in a trust-region setting has proven to be an effective method. In this paper, we present a Riemannian version of a limited-memory symmetric rank-one trust-region method with an efficient algorithm for solving its subproblem. Global convergence is shown to follow from a known standard result and numerical experiments are compare the proposed method with other Riemannian optimization methods.
Key words
Riemannian Optimization; limited-memory quasi-Newton; trust-region; Symmetric rank-one;
Status
Accepted in Proceeding of The 24st International Symposium on Mathematical Theory of Networks and Systems.
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BibTex entry
- Technical Report
@TECHREPORT{HG2020,
author = "Wen Huang and Kyle A. Gallivan",
title = "A limited-memory Riemannian symmetric rank-one trust-region method with an efficient algorithm for its subproblem",
year = 2020,
}