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Riemannian Optimization and Its ApplicationsConjugate Gradient Methods on Riemannian Manifolds

Riemannian Optimization and Its Applications: Conjugate Gradient Methods on Riemannian Manifolds [In this chapter, we discuss the conjugate gradient (CG) methods on Riemannian manifolds, which we also call Riemannian CG methods. They can be considered to be a modified version of the Riemannian steepest descent method. In particular, we analyze the Fletcher–Reeves-type and Dai–Yuan-type Riemannian CG methods and prove their global convergence properties under some conditions.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Riemannian Optimization and Its ApplicationsConjugate Gradient Methods on Riemannian Manifolds

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Publisher
Springer International Publishing
Copyright
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
ISBN
978-3-030-62389-0
Pages
65 –88
DOI
10.1007/978-3-030-62391-3_4
Publisher site
See Chapter on Publisher Site

Abstract

[In this chapter, we discuss the conjugate gradient (CG) methods on Riemannian manifolds, which we also call Riemannian CG methods. They can be considered to be a modified version of the Riemannian steepest descent method. In particular, we analyze the Fletcher–Reeves-type and Dai–Yuan-type Riemannian CG methods and prove their global convergence properties under some conditions.]

Published: Feb 18, 2021

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