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001446822 001__ 1446822
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001446822 020__ $$a9783031042935$$q(electronic bk.)
001446822 020__ $$a303104293X$$q(electronic bk.)
001446822 020__ $$z9783031042928
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001446822 0247_ $$a10.1007/978-3-031-04293-5$$2doi
001446822 035__ $$aSP(OCoLC)1319076389
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001446822 049__ $$aISEA
001446822 050_4 $$aQA649
001446822 08204 $$a516.3/73$$223/eng/20220601
001446822 1001_ $$aFong, Robert Simon,$$eauthor.
001446822 24510 $$aPopulation-based optimization on Riemannian manifolds /$$cRobert Simon Fong, Peter Tino.
001446822 264_1 $$aCham :$$bSpringer,$$c[2022]
001446822 264_4 $$c©2022
001446822 300__ $$a1 online resource
001446822 336__ $$atext$$btxt$$2rdacontent
001446822 337__ $$acomputer$$bc$$2rdamedia
001446822 338__ $$aonline resource$$bcr$$2rdacarrier
001446822 4901_ $$aStudies in computational intelligence ;$$vvolume 1046
001446822 504__ $$aIncludes bibliographical references and index.
001446822 5050_ $$aIntroduction -- Riemannian Geometry: A Brief Overview -- Elements of Information Geometry -- Probability Densities on Manifolds.
001446822 506__ $$aAccess limited to authorized users.
001446822 520__ $$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.
001446822 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed June 1, 2022).
001446822 650_0 $$aRiemannian manifolds.
001446822 650_0 $$aMathematical optimization.
001446822 655_0 $$aElectronic books.
001446822 7001_ $$aTino, Peter,$$eauthor.
001446822 77608 $$iPrint version: $$z3031042921$$z9783031042928$$w(OCoLC)1304356946
001446822 830_0 $$aStudies in computational intelligence ;$$vv. 1046.
001446822 852__ $$bebk
001446822 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-04293-5$$zOnline Access$$91397441.1
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001446822 980__ $$aBIB
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