This book explores discrete-time dynamic optimization and provides a detailed introduction to both deterministic and stochastic models. Covering problems with finite and infinite horizon, as well as Markov renewal programs, Bayesian control models and partially observable processes, the book focuses on the precise modelling of applications in a variety of areas, including operations research, computer science, mathematics, statistics, engineering, economics and finance.
Dynamic Optimization is a carefully presented textbook which starts with discrete-time deterministic dynamic optimization problems, providing readers with the tools for sequential decision-making, before proceeding to the more complicated stochastic models. The authors present complete and simple proofs and illustrate the main results with numerous examples and exercises (without solutions). With relevant material covered in four appendices, this book is completely self-contained.
Introduction and Organization of the Book.- Part I Deterministic Models.- Part II Markovian Decision Processes.- Part III Generalizations of Markovian Decision Processes.- Part IV Appendix.
Part I deals with deterministic dynamic optimization models describing the control of discrete-time systems. Part II is devoted to discrete-time stochastic control models. Part III & is devoted to Markovian decision processes with disturbances& The book comprises a lot of examples, problems for readers, and supplements with additional comments for the advanced reader and with bibliographic notes. (Svetlana A. Kravchenko, zbMATH 1365.90002)
Karl Hinderer was Professor of Stochastics at the Karlsruhe Institute of Technology KIT. He wrote the seminal book Foundations of Non-stationary Dynamic Programming with Discrete Time Parameter (1970) and the textlcC
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