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 ...
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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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New. Contains: Illustrations, black & white, Illustrations, color. Studies in Computational Intelligence . XI, 168 p. 24 illus., 17 illus. in color. Intended for professional and scholarly audience.
Add this copy of Population-Based Optimization on Riemannian Manifolds to cart. $131.49, new condition, Sold by GreatBookPrices rated 4.0 out of 5 stars, ships from Columbia, MD, UNITED STATES, published 2022 by Springer International Publishing AG.
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New. Contains: Illustrations, black & white, Illustrations, color. Studies in Computational Intelligence . XI, 168 p. 24 illus., 17 illus. in color. Intended for professional and scholarly audience. In Stock. 100% Money Back Guarantee. Brand New, Perfect Condition, allow 4-14 business days for standard shipping. To Alaska, Hawaii, U.S. protectorate, P.O. box, and APO/FPO addresses allow 4-28 business days for Standard shipping. No expedited shipping. All orders placed with expedited shipping will be cancelled. Over 3, 000, 000 happy customers.
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New. Contains: Illustrations, black & white, Illustrations, color. Studies in Computational Intelligence . XI, 168 p. 24 illus., 17 illus. in color. Intended for professional and scholarly audience.
Add this copy of Population-Based Optimization on Riemannian Manifolds to cart. $131.50, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2022 by Springer International Publishing AG.
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New. Print on demand Contains: Illustrations, black & white, Illustrations, color. Studies in Computational Intelligence . XI, 168 p. 24 illus., 17 illus. in color. Intended for professional and scholarly audience.
