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Algorithms for Worst-Case Design and Applications to Risk Management by Howe HC
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“Very good condition HC with DJ, little or no marking, not ex-library.”
En muy buen estado
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Ubicado en: Scottsboro, Alabama, Estados Unidos
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Entrega prevista entre el vie. 17 oct. y el mar. 21 oct. a 94104
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N.º de artículo de eBay:373218223551
Última actualización el 18 sep 2021 03:37:03 H.EspVer todas las actualizacionesVer todas las actualizaciones
Características del artículo
- Estado
- En muy buen estado
- Notas del vendedor
- “Very good condition HC with DJ, little or no marking, not ex-library.”
- ISBN
- 9780691091549
- EAN
- 9780691091549
Acerca de este producto
Product Identifiers
Publisher
Princeton University Press
ISBN-10
0691091544
ISBN-13
9780691091549
eBay Product ID (ePID)
2303705
Product Key Features
Number of Pages
408 Pages
Language
English
Publication Name
Algorithms for Worst-Case Design and Applications to Risk Management
Publication Year
2002
Subject
Decision-Making & Problem Solving, Algebra / General, Applied, Optimization
Type
Textbook
Subject Area
Mathematics, Business & Economics
Format
Hardcover
Dimensions
Item Height
1.2 in
Item Weight
27.5 Oz
Item Length
9.5 in
Item Width
6.5 in
Additional Product Features
Intended Audience
College Audience
LCCN
2004-301936
Reviews
"This book will be very helpful to those interested in uncertainty and robust decisions. I recommend it warmly to all practitioners and researchers in economics, environment, engineering design, finance and operations research." --P.M. Pardalos, Journal of Economics, "This book will be very helpful to those interested in uncertainty and robust decisions. I recommend it warmly to all practitioners and researchers in economics, environment, engineering design, finance and operations research."-- P.M. Pardalos, Journal of Economics, This book will be very helpful to those interested in uncertainty and robust decisions. I recommend it warmly to all practitioners and researchers in economics, environment, engineering design, finance and operations research. -- P.M. Pardalos, Journal of Economics, "This is minimax made practical, while maintaining theoretical rigor, computational feasibility, and good problem formulation. . . . The book is very comprehensive, and in many places quite detailed. However, it is easy to find a path through the material suited to one's purpose, ranging from a quick overview of this powerful approach to a detailed study of it and the relevant background material. It is an excellent example of how the results of an extensive research program can be translated into a book that is accessible and which is likely to have significant impact in both the optimization and finance communities." --David G. Luenberger, Journal of Economic Dynamics & Control, Written for postgraduate students and researchers engaged in optimization, engineering design, economics, and finance, this book will also be invaluable to practitioners in risk management. -- Zentralblatt MATH, "Written for postgraduate students and researchers engaged in optimization, engineering design, economics, and finance, this book will also be invaluable to practitioners in risk management." -- Zentralblatt MATH, "This is minimax made practical, while maintaining theoretical rigor, computational feasibility, and good problem formulation. . . . The book is very comprehensive, and in many places quite detailed. However, it is easy to find a path through the material suited to one's purpose, ranging from a quick overview of this powerful approach to a detailed study of it and the relevant background material. It is an excellent example of how the results of an extensive research program can be translated into a book that is accessible and which is likely to have significant impact in both the optimization and finance communities."-- David G. Luenberger, Journal of Economic Dynamics & Control, "Written for postgraduate students and researchers engaged in optimization, engineering design, economics, and finance, this book will also be invaluable to practitioners in risk management."-- Zentralblatt MATH, This book will be very helpful to those interested in uncertainty and robust decisions. I recommend it warmly to all practitioners and researchers in economics, environment, engineering design, finance and operations research., This is minimax made practical, while maintaining theoretical rigor, computational feasibility, and good problem formulation. . . . The book is very comprehensive, and in many places quite detailed. However, it is easy to find a path through the material suited to one's purpose, ranging from a quick overview of this powerful approach to a detailed study of it and the relevant background material. It is an excellent example of how the results of an extensive research program can be translated into a book that is accessible and which is likely to have significant impact in both the optimization and finance communities. -- David G. Luenberger, Journal of Economic Dynamics & Control, This is minimax made practical, while maintaining theoretical rigor, computational feasibility, and good problem formulation. . . . The book is very comprehensive, and in many places quite detailed. However, it is easy to find a path through the material suited to one's purpose, ranging from a quick overview of this powerful approach to a detailed study of it and the relevant background material. It is an excellent example of how the results of an extensive research program can be translated into a book that is accessible and which is likely to have significant impact in both the optimization and finance communities., Written for postgraduate students and researchers engaged in optimization, engineering design, economics, and finance, this book will also be invaluable to practitioners in risk management.
Dewey Edition
21
Dewey Decimal
511.8
Synopsis
Recognizing that robust decision making is vital in risk management, this book provides concepts and algorithms for computing the best decision in view of the worst-case scenario. The main tool used is minimax, which ensures robust policies with guaranteed optimal performance that will improve further if the worst case is not realized., Recognizing that robust decision making is vital in risk management, this book provides concepts and algorithms for computing the best decision in view of the worst-case scenario. The main tool used is minimax, which ensures robust policies with guaranteed optimal performace that will improve further if the worst case is not realized. The applications considered are drawn from finance, but the design and algorithms presented are equally applicable to problems of economic policy, engineering design, and other areas of decision making. Critically, worst-case design addresses not only Armageddon-type uncertainty. Indeed, the determination of the worst case becomes nontrivial when faced with numerous - possibly infinite - and reasonably likely rival scenarios. Optimality does not depend on any single scenario but on all the scenarios under consideration. Worst-case optimal decisions provide guaranteed optimal performance for systems operating within the specified scenario range indicating uncertainty. The noninferiority of minimax solutions - which also offer the possibility of multiple maxima - ensures this optimality.Worst-case design is not intended to necessarily replace expected, Recognizing that robust decision making is vital in risk management, this book provides concepts and algorithms for computing the best decision in view of the worst-case scenario. The main tool used is minimax, which ensures robust policies with guaranteed optimal performance that will improve further if the worst case is not realized. The applications considered are drawn from finance, but the design and algorithms presented are equally applicable to problems of economic policy, engineering design, and other areas of decision making. Critically, worst-case design addresses not only Armageddon-type uncertainty. Indeed, the determination of the worst case becomes nontrivial when faced with numerous--possibly infinite--and reasonably likely rival scenarios. Optimality does not depend on any single scenario but on all the scenarios under consideration. Worst-case optimal decisions provide guaranteed optimal performance for systems operating within the specified scenario range indicating the uncertainty. The noninferiority of minimax solutions--which also offer the possibility of multiple maxima--ensures this optimality. Worst-case design is not intended to necessarily replace expected value optimization when the underlying uncertainty is stochastic. However, wise decision making requires the justification of policies based on expected value optimization in view of the worst-case scenario. Conversely, the cost of the assured performance provided by robust worst-case decision making needs to be evaluated relative to optimal expected values. Written for postgraduate students and researchers engaged in optimization, engineering design, economics, and finance, this book will also be invaluable to practitioners in risk management.
LC Classification Number
HD61.R87 2002
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