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008			210217s2021 gw | s |||| 0|eng d
020			_a9783030623913
024	7		_a10.1007/978-3-030-62391-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _erda _dES-MaUEC
050		4	_aQA671 _b2021 EB
100	1		_aSatō, Hiroyuki _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9677847
245	1	0	_aRiemannian Optimization and Its Applications _cby Hiroyuki Sato
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Pulishing _c2021
300			_a1 recurso en línea (IX, 129 páginas) _b15 ilustraciones, 12 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2
490	0		_aSpringerBriefs in Control Automation and Robotics _x2192-6786
490	0		_aIntelligent Technologies and Robotics (SpringerNature-42732)
490	0		_aIntelligent Technologies and Robotics (R0) (SpringerNature-43728)
505	0		_aChapter 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	3		_aThis 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.
988			_aSpringer_Robotics_2021
650		7	_2embne _9677848 _aVariedades riemannianas
776	0	8	_iPrinted edition: _z9783030623890
776	0	8	_iPrinted edition: _z9783030623906
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-62391-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2021 _dz _eIG _zSI