9783031042928 - population-based optimization on riemannian manifolds: 1046 di fong, robert simon; tino, peter (18 risultati)
Lingua: Inglese
Editore: Springer, 2022
Serie: Studies in Computational Intelligence, Libro 523 di 538. Libro 523 di 538 - Studies in Computational Intelligence
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Condizione: New. In.
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Editore: Springer International Publishing AG, CH, 2022
Serie: Studies in Computational Intelligence, Libro 523 di 538. Libro 523 di 538 - Studies in Computational Intelligence
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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.
Lingua: Inglese
Editore: Springer, 2022
Serie: Studies in Computational Intelligence, Libro 523 di 538. Libro 523 di 538 - Studies in Computational Intelligence
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hardcover. Condizione: Very Good. Cover and edges may have some wear.
Lingua: Inglese
Editore: Springer, 2022
Serie: Studies in Computational Intelligence, Libro 523 di 538. Libro 523 di 538 - Studies in Computational Intelligence
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hardcover. Condizione: Sehr gut. 179 Seiten; 9783031042928.2 Gewicht in Gramm: 500.
Lingua: Inglese
Editore: Springer International Publishing AG, CH, 2022
Serie: Studies in Computational Intelligence, Libro 523 di 538. Libro 523 di 538 - Studies in Computational Intelligence
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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.
Lingua: Inglese
Editore: Springer, 2022
Serie: Studies in Computational Intelligence, Libro 523 di 538. Libro 523 di 538 - Studies in Computational Intelligence
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Serie: Studies in Computational Intelligence, Libro 523 di 538. Libro 523 di 538 - Studies in Computational Intelligence
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Serie: Studies in Computational Intelligence, Libro 523 di 538. Libro 523 di 538 - Studies in Computational Intelligence
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Condizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Springer, 2022
Serie: Studies in Computational Intelligence, Libro 523 di 538. Libro 523 di 538 - Studies in Computational Intelligence
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Condizione: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
Lingua: Inglese
Editore: Springer, Springer, 2022
Serie: Studies in Computational Intelligence, Libro 523 di 538. Libro 523 di 538 - Studies in Computational Intelligence
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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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.
Lingua: Inglese
Editore: Springer, 2022
Serie: Studies in Computational Intelligence, Libro 523 di 538. Libro 523 di 538 - Studies in Computational Intelligence
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Da: Buchpark, Trebbin, GermaniaBuchpark
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Condizione: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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.
Lingua: Inglese
Editore: Springer, 2022
Serie: Studies in Computational Intelligence, Libro 523 di 538. Libro 523 di 538 - Studies in Computational Intelligence
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Da: BUCHSERVICE / ANTIQUARIAT Lars Lutzer, Wahlstedt, GermaniaBUCHSERVICE / ANTIQUARIAT Lars Lutzer
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Hardcover. Condizione: gut. 2022. Population-Based Optimization on Riemannian Manifolds In deutscher Sprache. pages.
Lingua: Inglese
Editore: Springer, 2022
Serie: Studies in Computational Intelligence, Libro 523 di 538. Libro 523 di 538 - Studies in Computational Intelligence
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Condizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer, Springer Mai 2022, 2022
Serie: Studies in Computational Intelligence, Libro 523 di 538. Libro 523 di 538 - Studies in Computational Intelligence
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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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 ama…nifold.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.
Lingua: Inglese
Editore: Springer International Publishing, 2022
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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 R…iemannian manifolds .
Lingua: Inglese
Editore: Springer, Springer Mai 2022, 2022
Serie: Studies in Computational Intelligence, Libro 523 di 538. Libro 523 di 538 - Studies in Computational Intelligence
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. 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 man…ifold.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 KG, Sachsenplatz 4-6, 1201 Wien 180 pp. Englisch.
Lingua: Inglese
Editore: Springer, 2022
Serie: Studies in Computational Intelligence, Libro 523 di 538. Libro 523 di 538 - Studies in Computational Intelligence
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