- 178 pages
- English
- PDF
- Only available on web
Introduction to Stochastic Dynamic Programming
About This Book
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 existâproviding counterexamples where appropriateâand then presents methods for obtaining such policies when they do. In addition, general areas of application are presented. The final two chapters are concerned with more specialized models. These include stochastic scheduling models and a type of process known as a multiproject bandit. The mathematical prerequisites for this text are relatively few. No prior knowledge of dynamic programming is assumed and only a moderate familiarity with probabilityâ including the use of conditional expectationâis necessary.
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Table of contents
- Front Cover
- Introduction to Stochastic Dynamic Programming
- Copyright Page
- Table of Contents
- Dedication
- Preface
- Chapter I. Finite-Stage Models
- Chapter II. Discounted Dynamic Programming
- Chapter III. Minimizing CostsâNegative Dynamic Programming
- Chapter IV. Maximizing RewardsâPositive Dynamic Programming
- Chapter V. Average Reward Criterion
- Chapter VI. Stochastic Scheduling
- Chapter VII. Bandit Processes
- Appendix: Stochastic Order Relations
- Index