Dynamic Optimization
Deterministic and Stochastic Models
Published on 18. January 2017
Book
Paperback/Softback
XXII, 530 pages
978-3-319-48813-4 (ISBN)
Description
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.
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.
Reviews / Votes
"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)More details
Series
Edition
1st ed. 2016
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Primary & secondary/elementary & high school
Illustrations
22 s/w Abbildungen
XXII, 530 p. 22 illus.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 30 mm
Weight
826 gr
ISBN-13
978-3-319-48813-4 (9783319488134)
DOI
10.1007/978-3-319-48814-1
Schweitzer Classification
Other editions
Additional editions
Karl Hinderer | Ulrich Rieder | Michael Stieglitz
Dynamic Optimization
Deterministic and Stochastic Models
E-Book
01/2017
Springer
€96.29
Available for download
Persons
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 textbook
Grundbegriffe der Wahrscheinlichkeitstheorie
(1972). His main research areas were stochastic dynamic programming, probability and stochastic processes.
Ulrich Rieder is Professor emeritus at the University of Ulm. From 1990 to 2008, he was Editor-in-Chief of Mathematical Methods of Operations Research . His main research areas include stochastic dynamic programming and control, risk-sensitive Markov decision processes, stochastic games, and financial optimization.
Michael Stieglitz was Professor at the University of Karlsruhe until 2002. He contributes to summability, approximation theory, and probability.
Ulrich Rieder is Professor emeritus at the University of Ulm. From 1990 to 2008, he was Editor-in-Chief of Mathematical Methods of Operations Research . His main research areas include stochastic dynamic programming and control, risk-sensitive Markov decision processes, stochastic games, and financial optimization.
Michael Stieglitz was Professor at the University of Karlsruhe until 2002. He contributes to summability, approximation theory, and probability.
Content
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.