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Riemannian optimization and its appl...

Sato, Hiroyuki.

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Riemannian optimization and its applications

Record Type:	Electronic resources : Monograph/item
Title/Author:	Riemannian optimization and its applications/ by Hiroyuki Sato.
Author:	Sato, Hiroyuki.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	ix, 129 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1. Introduction -- Chapter 2. Preliminaries and Overview of Euclidean Optimization -- Chapter 3. Unconstrained Optimization on Riemannian Manifolds -- Chapter 4. Conjugate Gradient Methods on Riemannian Manifolds -- Chapter 5. Applications of Riemannian Optimization -- Chapter 6. Recent Developments in Riemannian Optimization.
Contained By:	Springer Nature eBook
Subject:	Riemannian manifolds. -
Online resource:	https://doi.org/10.1007/978-3-030-62391-3
ISBN:	9783030623913

Riemannian optimization and its applications
Sato, Hiroyuki.

Riemannian optimization and its applications[electronic resource] /by Hiroyuki Sato. - Cham :Springer International Publishing :2021. - ix, 129 p. :ill., digital ;24 cm. - SpringerBriefs in electrical and computer engineering, Control, automation and robotics. - SpringerBriefs in electrical and computer engineering.Control, automation and robotics..

Chapter 1. Introduction -- Chapter 2. Preliminaries and Overview of Euclidean Optimization -- Chapter 3. Unconstrained Optimization on Riemannian Manifolds -- Chapter 4. Conjugate Gradient Methods on Riemannian Manifolds -- Chapter 5. Applications of Riemannian Optimization -- Chapter 6. Recent Developments in Riemannian Optimization.

This brief describes the basics of Riemannian optimization-optimization on Riemannian manifolds-introduces algorithms for Riemannian optimization problems, discusses the theoretical properties of these algorithms, and suggests possible applications of Riemannian optimization to problems in other fields. To provide the reader with a smooth introduction to Riemannian optimization, brief reviews of mathematical optimization in Euclidean spaces and Riemannian geometry are included. Riemannian optimization is then introduced by merging these concepts. In particular, the Euclidean and Riemannian conjugate gradient methods are discussed in detail. A brief review of recent developments in Riemannian optimization is also provided. Riemannian optimization methods are applicable to many problems in various fields. This brief discusses some important applications including the eigenvalue and singular value decompositions in numerical linear algebra, optimal model reduction in control engineering, and canonical correlation analysis in statistics.

ISBN: 9783030623913

Standard No.: 10.1007/978-3-030-62391-3doiSubjects--Topical Terms:

540526
Riemannian manifolds.

LC Class. No.: QA649 / .S38 2021

Dewey Class. No.: 516.373

Riemannian optimization and its applications
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245 1 0 $a Riemannian optimization and its applications $h [electronic resource] / $c by Hiroyuki Sato.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a ix, 129 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in electrical and computer engineering, Control, automation and robotics
505 0 $a Chapter 1. Introduction -- Chapter 2. Preliminaries and Overview of Euclidean Optimization -- Chapter 3. Unconstrained Optimization on Riemannian Manifolds -- Chapter 4. Conjugate Gradient Methods on Riemannian Manifolds -- Chapter 5. Applications of Riemannian Optimization -- Chapter 6. Recent Developments in Riemannian Optimization.
520 $a This brief describes the basics of Riemannian optimization-optimization on Riemannian manifolds-introduces algorithms for Riemannian optimization problems, discusses the theoretical properties of these algorithms, and suggests possible applications of Riemannian optimization to problems in other fields. To provide the reader with a smooth introduction to Riemannian optimization, brief reviews of mathematical optimization in Euclidean spaces and Riemannian geometry are included. Riemannian optimization is then introduced by merging these concepts. In particular, the Euclidean and Riemannian conjugate gradient methods are discussed in detail. A brief review of recent developments in Riemannian optimization is also provided. Riemannian optimization methods are applicable to many problems in various fields. This brief discusses some important applications including the eigenvalue and singular value decompositions in numerical linear algebra, optimal model reduction in control engineering, and canonical correlation analysis in statistics.
650 0 $a Riemannian manifolds. $3 540526
650 0 $a Mathematical optimization. $3 517763
650 1 4 $a Control and Systems Theory. $3 3381515
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650 2 4 $a Statistical Theory and Methods. $3 891074
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650 2 4 $a Linear and Multilinear Algebras, Matrix Theory. $3 891082
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950 $a Intelligent Technologies and Robotics (SpringerNature-42732)