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Introduction to Stochastic Dynamic Programming.

Author: Sheldon M Ross; Z W Birnbaum; E Lukacs
Publisher: Burlington : Elsevier Science, 2014.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Introduction to Stochastic Dynamic Programming presents the basic theory and examines the scope of applications of stochastic dynamic programming. The book begins with a chapter on various finite-stage models, illustrating the wide range of applications of stochastic dynamic programming. Subsequent chapters study infinite-stage models: discounting future returns, minimizing nonnegative costs, maximizing nonnegative  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Ross, Sheldon M.
Introduction to Stochastic Dynamic Programming.
Burlington : Elsevier Science, ©2014
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Sheldon M Ross; Z W Birnbaum; E Lukacs
ISBN: 9781483269092 1483269094
OCLC Number: 898769200
Description: 1 online resource (179 pages)
Contents: Front Cover; Introduction to Stochastic Dynamic Programming; Copyright Page; Dedication; Table of Contents; Preface; Chapter I. Finite-Stage Models; 1. Introduction; 2. A Gambling Model; 3. A Stock-Option Model; 4. Modular Functions and Monotone Policies; 5. Accepting the Best Offer; 6. A Sequential Allocation Model; 7. The Interchange Argument in Sequencing; Problems; Notes and References; Chapter II. Discounted Dynamic Programming; 1. Introduction; 2. The Optimality Equation and Optimal Policy; 3. Method of Successive Approximations; 4. Policy Improvement; 5. Solution by Linear Programming. 6. Extension to Unbounded RewardsProblems; References; Chapter III. Minimizing Costs-Negative Dynamic Programming; 1. Introduction and Some Theoretical Results; 2. Optimal Stopping Problems; 3. Bayesian Sequential Analysis; 4. Computational Approaches; 5. Optimal Search; Problems; References; Chapter IV. Maximizing Rewards-Positive Dynamic Programming; 1. Introduction and Main Theoretical Results; 2. Applications to Gambling Theory; 3. Computational Approaches to Obtaining V; Problems; Notes and References; Chapter V. Average Reward Criterion; 1. Introduction and Counterexamples. 2. Existence of an Optimal Stationary Policy3. Computational Approaches; Problems; Notes and References; Chapter VI. Stochastic Scheduling; 1. Introduction; 2. Maximizing Finite-Time Returns-Single Processor; 3. Minimizing Expected Makespan-Processors in Parallel; 4. Minimizing Expected Makespan-Processors in Series; 5. Maximizing Total Field Life; 6. A Stochastic Knapsack Model; 7. A Sequential-Assignment Problem; Problems; Notes and References; Chapter VII. Bandit Processes; 1. Introduction; 2. Single-Project Bandit Processes; 3. Multiproject Bandit Processes. 4. An Extension and a Nonextension5. Generalizations of the Classical Bandit Problem; Problems; Notes and References; Appendix: Stochastic Order Relations; 1. Stochastically Larger; 2. Coupling; 3. Hazard-Rate Ordering; 4. Likelihood-Ratio Ordering; Problems; Reference; Index.

Abstract:

Introduction to Stochastic Dynamic Programming presents the basic theory and examines the scope of applications of stochastic dynamic programming. The book begins with a chapter on various finite-stage models, illustrating the wide range of applications of stochastic dynamic programming. Subsequent chapters study infinite-stage models: discounting future returns, minimizing nonnegative costs, maximizing nonnegative returns, and maximizing the long-run average return. Each of these chapters first considers whether an optimal policy need exist-providing counterexamples where appropriate-and the.

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