Articles liés à Population-based Optimization on Riemannian Manifolds
Synopsis
Introduction.- Riemannian Geometry: A Brief Overview.- Elements of Information Geometry.- Probability Densities on Manifolds.
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Population-Based Optimization on Riemannian Manifolds (Studies in Computational Intelligence, 1046)
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Springer, 2022
ISBN 10 : 3031042921
ISBN 13 : 9783031042928
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Vendeur : Books From California, Simi Valley, CA, Etats-Unis
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hardcover. Etat : Very Good. Cover and edges may have some wear. N° de réf. du vendeur mon0003813126
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Population-Based Optimization on Riemannian Manifolds (Studies in Computational Intelligence, 1046)
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Population-Based Optimization on Riemannian Manifolds
Edité par
Springer International Publishing AG, CH, 2022
ISBN 10 : 3031042921
ISBN 13 : 9783031042928
Neuf
Couverture rigide
Vendeur : Rarewaves.com USA, London, LONDO, Royaume-Uni
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Hardback. Etat : 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. N° de réf. du vendeur LU-9783031042928
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Population-based Optimization on Riemannian Manifolds
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Population-Based Optimization on Riemannian Manifolds (Studies in Computational Intelligence, 1046)
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Population-Based Optimization on Riemannian Manifolds (Studies in Computational Intelligence, 1046)
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Population-Based Optimization on Riemannian Manifolds (Hardcover)
Edité par
Springer International Publishing AG, Cham, 2022
ISBN 10 : 3031042921
ISBN 13 : 9783031042928
Neuf
Couverture rigide
Vendeur : Grand Eagle Retail, Mason, OH, Etats-Unis
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Hardcover. Etat : new. Hardcover. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9783031042928
Quantité disponible : 1 disponible(s)
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Population-based Optimization on Riemannian Manifolds
Edité par
Springer, 2022
ISBN 10 : 3031042921
ISBN 13 : 9783031042928
Ancien ou d'occasion
Couverture rigide
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 44532466
Quantité disponible : 15 disponible(s)
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Population-Based Optimization on Riemannian Manifolds
Edité par
Springer International Publishing AG, CH, 2022
ISBN 10 : 3031042921
ISBN 13 : 9783031042928
Neuf
Couverture rigide
Vendeur : Rarewaves.com UK, London, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Hardback. Etat : 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. N° de réf. du vendeur LU-9783031042928
Quantité disponible : Plus de 20 disponibles
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Population-Based Optimization on Riemannian Manifolds
Edité par
Springer International Publishing Mai 2022, 2022
ISBN 10 : 3031042921
ISBN 13 : 9783031042928
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : 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. N° de réf. du vendeur 9783031042928
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