Author:
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 866

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Author:
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 308

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Author: Hiroshima Daigaku
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 616

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Author: Hiroshima Daigaku
Publisher:
ISBN:
Category : Science
Languages : en
Pages : 322

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Author:
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 696

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Author: Hiroshima Daigaku
Publisher:
ISBN:
Category : Science
Languages : en
Pages : 890

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Author: Hiroshima Daigaku
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 592

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Book Description

Author: Wolfgang Arendt
Publisher: John Wiley & Sons
ISBN: 3527628037
Category : Science
Languages : en
Pages : 502

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Book Description
Mathematical Analysis of Evolution, Information, and Complexity deals with the analysis of evolution, information and complexity. The time evolution of systems or processes is a central question in science, this text covers a broad range of problems including diffusion processes, neuronal networks, quantum theory and cosmology. Bringing together a wide collection of research in mathematics, information theory, physics and other scientific and technical areas, this new title offers elementary and thus easily accessible introductions to the various fields of research addressed in the book.

Author: Takeyuki Hida
Publisher: World Scientific
ISBN: 9789812380203
Category : Science
Languages : en
Pages : 212

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Book Description
Annotation. ...study on the Power of Potential fluctuation in living cells...some properties of measure-valued processes with singular branching rate and other papers.

Author: Robert Simon Fong
Publisher: Springer Nature
ISBN: 303104293X
Category : Technology & Engineering
Languages : en
Pages : 171

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Book Description
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.