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.
