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Manifold 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.
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Manifold 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.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Population-Based Optimization on Riemannian Manifolds (Studies in Computational Intelligence, 1046)
Da: Ria Christie Collections, Uxbridge, Regno Unito
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Population-Based Optimization on Riemannian Manifolds
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ISBN 13: 9783031042928
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Hardback. Condizione: New. 2022 ed. Manifold 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. Codice articolo LU-9783031042928
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Population-Based Optimization on Riemannian Manifolds
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Springer International Publishing AG, CH, 2022
ISBN 10: 3031042921
ISBN 13: 9783031042928
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Hardback. Condizione: New. 2022 ed. Manifold 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. Codice articolo LU-9783031042928
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Population-Based Optimization on Riemannian Manifolds (Studies in Computational Intelligence, 1046)
Da: Books From California, Simi Valley, CA, U.S.A.
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hardcover. Condizione: Very Good. Cover and edges may have some wear. Codice articolo mon0003813126
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Population-Based Optimization on Riemannian Manifolds
Editore:
Springer International Publishing, 2022
ISBN 10: 3031042921
ISBN 13: 9783031042928
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Condizione: Hervorragend. Zustand: Hervorragend | Seiten: 180 | Sprache: Englisch | Produktart: Bücher. Codice articolo 38794602/1
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Population-based Optimization on Riemannian Manifolds
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents recent research on Population-based Optimization on Riemannian manifolds Addresses the locality and implicit assumptions of manifold optimization Presents a novel population-based optimization algorithm on Riemannian manifolds . Codice articolo 575523153
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Population-Based Optimization on Riemannian Manifolds
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Springer International Publishing Mai 2022, 2022
ISBN 10: 3031042921
ISBN 13: 9783031042928
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Manifold optimization is an emerging field of contemporary optimization thatconstructs efficient and robust algorithms by exploiting the specific geometricalstructure of the search space. In our case the search space takes the form of amanifold.Manifold optimization methods mainly focus on adapting existing optimizationmethods from the usual 'easy-to-deal-with' Euclidean search spaces to manifoldswhose local geometry can be defined e.g. by a Riemannian structure. In this waythe form of the adapted algorithms can stay unchanged. However, to accommodatethe adaptation process, assumptions on the search space manifold often have tobe made. In addition, the computations and estimations are confined by the localgeometry.This book presents a framework for population-based optimization on Riemannianmanifolds that overcomes both the constraints of locality and additional assumptions.Multi-modal, black-box manifold optimization problems on Riemannian manifoldscan be tackled using zero-order stochastic optimization methods from a geometricalperspective, utilizing both the statistical geometry of the decision spaceand Riemannian geometry of the search space.This monograph presents in a self-contained manner both theoretical and empiricalaspects ofstochastic population-based optimization on abstract Riemannianmanifolds. 180 pp. Englisch. Codice articolo 9783031042928
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Population-Based Optimization on Riemannian Manifolds
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Springer International Publishing, 2022
ISBN 10: 3031042921
ISBN 13: 9783031042928
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Manifold optimization is an emerging field of contemporary optimization thatconstructs efficient and robust algorithms by exploiting the specific geometricalstructure of the search space. In our case the search space takes the form of amanifold.Manifold optimization methods mainly focus on adapting existing optimizationmethods from the usual 'easy-to-deal-with' Euclidean search spaces to manifoldswhose local geometry can be defined e.g. by a Riemannian structure. In this waythe form of the adapted algorithms can stay unchanged. However, to accommodatethe adaptation process, assumptions on the search space manifold often have tobe made. In addition, the computations and estimations are confined by the localgeometry.This book presents a framework for population-based optimization on Riemannianmanifolds that overcomes both the constraints of locality and additional assumptions.Multi-modal, black-box manifold optimization problems on Riemannian manifoldscan be tackled using zero-order stochastic optimization methods from a geometricalperspective, utilizing both the statistical geometry of the decision spaceand Riemannian geometry of the search space.This monograph presents in a self-contained manner both theoretical and empiricalaspects ofstochastic population-based optimization on abstract Riemannianmanifolds. Codice articolo 9783031042928
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Population-Based Optimization on Riemannian Manifolds
ISBN 10: 3031042921
ISBN 13: 9783031042928
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Buch. Condizione: Neu. Neuware -Manifold 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 180 pp. Englisch. Codice articolo 9783031042928
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Population-Based Optimization on Riemannian Manifolds (Studies in Computational Intelligence, 1046)
Da: California Books, Miami, FL, U.S.A.
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Condizione: New. Codice articolo I-9783031042928
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