Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This monograph presents a comprehensive treatment of the maximum-entropy sampling problem (MESP), which is a fascinating topic at the intersection of mathematical optimizationand data science. The text situates MESP in information theory, as the algorithmic problem of calculating a sub-vector of pre-specificedsize from a multivariate Gaussian random vector, so as to maximize Shannon's differential entropy. The text collects and expands on state-of-the-art algorithms for MESP, and addresses its application in the field of environmental monitoring. While MESP is a central optimization problem in the theory of statistical designs (particularly in the area of spatial monitoring), this book largely focuses on the unique challenges of its algorithmic side. From the perspective of mathematical-optimization methodology, MESP is rather unique (a 0/1 nonlinear program having a nonseparable objective function), and the algorithmic techniques employed are highly non-standard. In particular, successful techniques come from several disparate areas within the field of mathematical optimization; for example: convex optimization and duality, semidefinite programming, Lagrangian relaxation, dynamic programming, approximation algorithms, 0/1 optimization (e.g., branch-and-bound), extended formulation, and many aspects of matrix theory. The book is mainly aimed at graduate students and researchers in mathematical optimization and data analytics. 216 pp. Englisch. Seller Inventory # 9783031130809

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Maximum-Entropy Sampling : Algorithms and Application

Jon Lee

Published by Springer International Publishing, 2023

ISBN 10: 3031130804 ISBN 13: 9783031130809

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Seller: AHA-BUCH GmbH, Einbeck, Germany

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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This monograph presents a comprehensive treatment of the maximum-entropy sampling problem (MESP), which is a fascinating topic at the intersection of mathematical optimizationand data science. The text situates MESP in information theory, as the algorithmic problem of calculating a sub-vector of pre-specificedsize from a multivariate Gaussian random vector, so as to maximize Shannon's differential entropy. The text collects and expands on state-of-the-art algorithms for MESP, and addresses its application in the field of environmental monitoring. While MESP is a central optimization problem in the theory of statistical designs (particularly in the area of spatial monitoring), this book largely focuses on the unique challenges of its algorithmic side. From the perspective of mathematical-optimization methodology, MESP is rather unique (a 0/1 nonlinear program having a nonseparable objective function), and the algorithmic techniques employed are highly non-standard. In particular, successful techniques come from several disparate areas within the field of mathematical optimization; for example: convex optimization and duality, semidefinite programming, Lagrangian relaxation, dynamic programming, approximation algorithms, 0/1 optimization (e.g., branch-and-bound), extended formulation, and many aspects of matrix theory. The book is mainly aimed at graduate students and researchers in mathematical optimization and data analytics. Seller Inventory # 9783031130809

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Maximum-Entropy Sampling

Marcia Fampa|Jon Lee

Published by Springer International Publishing, 2023

ISBN 10: 3031130804 ISBN 13: 9783031130809

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Seller: moluna, Greven, Germany

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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Aimed at graduate students and researchers in optimization and data analyticsConcerns the MESP at the intersection of optimization algorithms and data scienceThere is no other book on this subjectThis monograph presents a comprehens. Seller Inventory # 1155871994

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