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.