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![]( "Buchcover ISBN 9780470182956")
"Perhaps the most appealing aspect of Professor Powell's bookis the fact that it spans both theory and practice. Problems, deemed intractable a few years ago, are now easily solved by usingthe exhibited techniques in this book. I would stronglyrecommend the book to any practitioner facing complex, dynamicmodels involving constantly changing information streams.„ (IIETransactions-Operations Engineering, 2008)“Focus[es] on the core. of dynamic programming with asimple and clear exposition of the material. while. elevating the standard of the theory.„*(Computing Reviews, May 5, 2008)“Motivated by examples from modern-day operationsresearch, Approximate Dynamic Programming is anaccessible introduction to dynamic modeling and is also a valuableguide for the development of high-quality solutions to problemsthat exist in operations research and engineering. (Mathematical Reviews, 2008)
A complete and accessible introduction to the real-worldapplications of approximate dynamic programming
With the growing levels of sophistication in modern-dayoperations, it is vital for practitioners to understand how toapproach, model, and solve complex industrial problems. ApproximateDynamic Programming is a result of the author's decades ofexperience working in large industrial settings to developpractical and high-quality solutions to problems that involvemaking decisions in the presence of uncertainty. Thisgroundbreaking book uniquely integrates four distinctdisciplines--Markov design processes, mathematicalprogramming, simulation, and statistics--to demonstrate how tosuccessfully model and solve a wide range of real-life problemsusing the techniques of approximate dynamic programming (ADP). Thereader is introduced to the three curses of dimensionality thatimpact complex problems and is also shown how the post-decisionstate variable allows for the use of classical algorithmicstrategies from operations research to treat complex stochasticoptimization problems.
Designed as an introduction and assuming no prior training indynamic programming of any form, Approximate Dynamic Programmingcontains dozens of algorithms that are intended to serve as astarting point in the design of practical solutions for realproblems. The book provides detailed coverage of implementationchallenges including: modeling complex sequential decisionprocesses under uncertainty, identifying robust policies, designingand estimating value function approximations, choosing effectivestepsize rules, and resolving convergence issues.
With a focus on modeling and algorithms in conjunction with thelanguage of mainstream operations research, artificialintelligence, and control theory, Approximate DynamicProgramming:
Motivated by examples from modern-day operations research, Approximate Dynamic Programming is an accessible introduction todynamic modeling and is also a valuable guide for the developmentof high-quality solutions to problems that exist in operationsresearch and engineering. The clear and precise presentation of thematerial makes this an appropriate text for advanced undergraduateand beginning graduate courses, while also serving as a referencefor researchers and practitioners. A companion Web site isavailable for readers, which includes additional exercises, solutions to exercises, and data sets to reinforce the book's mainconcepts.
With the growing levels of sophistication in modern-dayoperations, it is vital for practitioners to understand how toapproach, model, and solve complex industrial problems. ApproximateDynamic Programming is a result of the author's decades ofexperience working in large industrial settings to developpractical and high-quality solutions to problems that involvemaking decisions in the presence of uncertainty. Thisgroundbreaking book uniquely integrates four distinctdisciplines--Markov design processes, mathematicalprogramming, simulation, and statistics--to demonstrate how tosuccessfully model and solve a wide range of real-life problemsusing the techniques of approximate dynamic programming (ADP). Thereader is introduced to the three curses of dimensionality thatimpact complex problems and is also shown how the post-decisionstate variable allows for the use of classical algorithmicstrategies from operations research to treat complex stochasticoptimization problems.
Designed as an introduction and assuming no prior training indynamic programming of any form, Approximate Dynamic Programmingcontains dozens of algorithms that are intended to serve as astarting point in the design of practical solutions for realproblems. The book provides detailed coverage of implementationchallenges including: modeling complex sequential decisionprocesses under uncertainty, identifying robust policies, designingand estimating value function approximations, choosing effectivestepsize rules, and resolving convergence issues.
With a focus on modeling and algorithms in conjunction with thelanguage of mainstream operations research, artificialintelligence, and control theory, Approximate DynamicProgramming:
Motivated by examples from modern-day operations research, Approximate Dynamic Programming is an accessible introduction todynamic modeling and is also a valuable guide for the developmentof high-quality solutions to problems that exist in operationsresearch and engineering. The clear and precise presentation of thematerial makes this an appropriate text for advanced undergraduateand beginning graduate courses, while also serving as a referencefor researchers and practitioners. A companion Web site isavailable for readers, which includes additional exercises, solutions to exercises, and data sets to reinforce the book's mainconcepts.