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020			_a9783031042935
024	7		_a10.1007/978-3-031-04293-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA649 _b2022 EB
100	1		_aFong, Robert Simon _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9684499
245	1	0	_aPopulation-Based Optimization on Riemannian Manifolds _cby Robert Simon Fong, Peter Tino
250			_aFirst edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XI, 168 páginas) _b24 ilustraciones, 17 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aStudies in Computational Intelligence _x1860-9503 _v1046
505	0		_aIntroduction -- Riemannian Geometry: A Brief Overview -- Elements of Information Geometry -- Probability Densities on Manifolds.
520			_aManifold optimization is an emerging field of contemporary optimization that constructs efficient and robust algorithms by exploiting the specific geometrical structure of the search space. In our case the search space takes the form of a manifold. Manifold optimization methods mainly focus on adapting existing optimization methods from the usual "easy-to-deal-with" Euclidean search spaces to manifolds whose local geometry can be defined e.g. by a Riemannian structure. In this way the form of the adapted algorithms can stay unchanged. However, to accommodate the adaptation process, assumptions on the search space manifold often have to be made. In addition, the computations and estimations are confined by the local geometry. This book presents a framework for population-based optimization on Riemannian manifolds that overcomes both the constraints of locality and additional assumptions. Multi-modal, black-box manifold optimization problems on Riemannian manifolds can be tackled using zero-order stochastic optimization methods from a geometrical perspective, utilizing both the statistical geometry of the decision space and Riemannian geometry of the search space. This monograph presents in a self-contained manner both theoretical and empirical aspects of stochastic population-based optimization on abstract Riemannian manifolds.
988			_aSpringer_Robotics_2022
650		7	_2embne _9677848 _aVariedades riemannianas
650		7	_2embne _9140354 _aGeometría diferencial
700	1		_aTino, Peter _eautor _0(orcid)0000-0003-2330-128X _1https://orcid.org/0000-0003-2330-128X _4aut _4http://id.loc.gov/vocabulary/relators/aut _9684500
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783031042928
776	0	8	_iPrinted edition: _z9783031042942
776	0	8	_iPrinted edition: _z9783031042959
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-04293-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b07/2022 _dz _eIG _zSI