Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient perturbation approaches form a thread unifying all the algorithms considered. Simultaneous perturbation stochastic approximation and smooth fractional estimators for gradient- and Hessian-based methods are presented. These algorithms:
• are easily implemented;
• do not require an explicit system model; and
• work with real or simulated data.
Chapters on their application in service systems, vehicular traffic control and communications networks illustrate this point. The book is self-contained with necessary mathematical results placed in an appendix.
The text provides easy-to-use, off-the-shelf algorithms that are given detailed mathematical treatment so the material presented will be of significant interest to practitioners, academic researchers and graduate students alike. The breadth of applications makes the book appropriate for reader from similarly diverse backgrounds: workers in relevant areas of computer science, control engineering, management science, applied mathematics, industrial engineering and operations research will find the content of value.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
All three authors have been extensively working in the area of stochastic control and optimization. S. Bhatnagar has worked for nearly 20 years in this area and has published extensively in both journals and conferences. This book in many ways summarizes the various strands of research that S.Bhatnagar has been involved in over the last decade. H.L.Prasad and Prashanth L.A. have been working in this area for over five years now and have been actively involved in various aspects of the research reported here. The entire book, in many ways, is a collection of the various strands of the research that has been primarily carried out by the authors themselves during the course of the last several years.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient perturbation approaches form a thread unifying all the algorithms considered. Simultaneous perturbation stochastic approximation and smooth fractional estimators for gradient- and Hessian-based methods are presented. These algorithms:- are easily implemented;- do not require an explicit system model; and- work with real or simulated data.Chapters on their application in service systems, vehicular traffic control and communications networks illustrate this point. The book is self-contained with necessary mathematical results placed in an appendix.The text provides easy-to-use, off-the-shelf algorithms that are given detailed mathematical treatment so the material presented will be of significant interest to practitioners, academic researchers and graduate students alike. The breadth of applications makes the book appropriate for reader from similarly diverse backgrounds: workers in relevant areas of computer science, control engineering, management science, applied mathematics, industrial engineering and operations research will find the content of value. 320 pp. Englisch. N° de réf. du vendeur 9781447142843
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Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Algorithms described perform better in real-life settings than many previously described in the literature Detailed mathematical treatment of the algorithms proposed is provided using both gradient- and Hessian-based methods Both constraine. N° de réf. du vendeur 4184917
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Taschenbuch. Etat : Neu. Stochastic Recursive Algorithms for Optimization | Simultaneous Perturbation Methods | S. Bhatnagar (u. a.) | Taschenbuch | Lecture Notes in Control and Information Sciences | xvii | Englisch | 2012 | Springer | EAN 9781447142843 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. N° de réf. du vendeur 106442086
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient perturbation approaches form a thread unifying all the algorithms considered. Simultaneous perturbation stochastic approximation and smooth fractional estimators for gradient- and Hessian-based methods are presented. These algorithms:¿ are easily implemented;¿ do not require an explicit system model; and¿ work with real or simulated data.Chapters on their application in service systems, vehicular traffic control and communications networks illustrate this point. The book is self-contained with necessary mathematical results placed in an appendix.The text provides easy-to-use, off-the-shelf algorithms that are given detailed mathematical treatment so the material presented will be of significant interest to practitioners, academic researchers and graduate students alike. The breadth of applications makes the book appropriate for reader from similarly diverse backgrounds: workers in relevant areas of computer science, control engineering, management science, applied mathematics, industrial engineering and operations research will find the content of value.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 320 pp. Englisch. N° de réf. du vendeur 9781447142843
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient perturbation approaches form a thread unifying all the algorithms considered. Simultaneous perturbation stochastic approximation and smooth fractional estimators for gradient- and Hessian-based methods are presented. These algorithms:- are easily implemented;- do not require an explicit system model; and- work with real or simulated data.Chapters on their application in service systems, vehicular traffic control and communications networks illustrate this point. The book is self-contained with necessary mathematical results placed in an appendix.The text provides easy-to-use, off-the-shelf algorithms that are given detailed mathematical treatment so the material presented will be of significant interest to practitioners, academic researchers and graduate students alike. The breadth of applications makes the book appropriate for reader from similarly diverse backgrounds: workers in relevant areas of computer science, control engineering, management science, applied mathematics, industrial engineering and operations research will find the content of value. N° de réf. du vendeur 9781447142843
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