Dynamic Optimization
Deterministic and Stochastic Models

Authors: Karl Hinderer, Ulrich Rieder, Michael Stieglitz



Provides a self-contained and easy-to-read introduction to dynamic programming

Provides a comprehensive treatment of discrete-time multistage optimization

Presents the theory of Markov decision processes without advanced measure theory

Includes various examples and exercises (without solutions)

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.

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