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Studies in Computational Intelligence Ser.: Population-Based Optimization on Riemannian Manifolds by Peter Tino and Robert Simon Fong (2022, Hardcover)
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Artikelzustand:
- Sofort-KaufenRobert Fong, Peter Tino: Populationsbasierte Optimierung an Riemannschen Mannigfaltigkeiten
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Über dieses Produkt
Product Identifiers
PublisherSpringer International Publishing A&G
ISBN-103031042921
ISBN-139783031042928
eBay Product ID (ePID)6057300089
Product Key Features
Number of PagesXi, 168 Pages
Publication NamePopulation-Based Optimization on Riemannian Manifolds
LanguageEnglish
Publication Year2022
SubjectEngineering (General), Intelligence (Ai) & Semantics, General
TypeTextbook
AuthorPeter Tino, Robert Simon Fong
Subject AreaMathematics, Computers, Technology & Engineering
SeriesStudies in Computational Intelligence Ser.
FormatHardcover
Dimensions
Item Weight15.6 Oz
Item Length9.3 in
Item Width6.1 in
Additional Product Features
Dewey Edition23
Series Volume Number1046
Number of Volumes1 vol.
IllustratedYes
Dewey Decimal516.373
Table Of ContentIntroduction.- Riemannian Geometry: A Brief Overview.- Elements of Information Geometry.- Probability Densities on Manifolds.
SynopsisManifold 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.
LC Classification NumberQ342
