Table of contents
About this book
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 programming Markov decision processes discrete-time multi-stage optimization networks stochastic optimal control Markov renewal programs Bayesian control models partially observable processes
- DOI https://doi.org/10.1007/978-3-319-48814-1
- Copyright Information Springer International Publishing AG 2016
- Publisher Name Springer, Cham
- eBook Packages Mathematics and Statistics Mathematics and Statistics (R0)
- Print ISBN 978-3-319-48813-4
- Online ISBN 978-3-319-48814-1
- Series Print ISSN 0172-5939
- Series Online ISSN 2191-6675
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