Dynamic Optimization

Deterministic and Stochastic Models

  • Karl Hinderer
  • Ulrich Rieder
  • Michael Stieglitz

Part of the Universitext book series (UTX)

Table of contents

  1. Front Matter
    Pages i-xxii
  2. Karl Hinderer, Ulrich Rieder, Michael Stieglitz
    Pages 1-11
  3. Deterministic Models

    1. Front Matter
      Pages 13-13
    2. Karl Hinderer, Ulrich Rieder, Michael Stieglitz
      Pages 15-33
    3. Karl Hinderer, Ulrich Rieder, Michael Stieglitz
      Pages 35-49
    4. Karl Hinderer, Ulrich Rieder, Michael Stieglitz
      Pages 51-68
    5. Karl Hinderer, Ulrich Rieder, Michael Stieglitz
      Pages 69-86
    6. Karl Hinderer, Ulrich Rieder, Michael Stieglitz
      Pages 87-104
    7. Karl Hinderer, Ulrich Rieder, Michael Stieglitz
      Pages 105-123
    8. Karl Hinderer, Ulrich Rieder, Michael Stieglitz
      Pages 125-148
    9. Karl Hinderer, Ulrich Rieder, Michael Stieglitz
      Pages 149-167
    10. Karl Hinderer, Ulrich Rieder, Michael Stieglitz
      Pages 169-186
  4. Markovian Decision Processes

    1. Front Matter
      Pages 187-187
    2. Karl Hinderer, Ulrich Rieder, Michael Stieglitz
      Pages 189-198
    3. Karl Hinderer, Ulrich Rieder, Michael Stieglitz
      Pages 199-219
    4. Karl Hinderer, Ulrich Rieder, Michael Stieglitz
      Pages 221-257
    5. Karl Hinderer, Ulrich Rieder, Michael Stieglitz
      Pages 259-275
    6. Karl Hinderer, Ulrich Rieder, Michael Stieglitz
      Pages 277-289
    7. Karl Hinderer, Ulrich Rieder, Michael Stieglitz
      Pages 291-318
    8. Karl Hinderer, Ulrich Rieder, Michael Stieglitz
      Pages 319-325

About this book

Introduction

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.

Keywords

dynamic programming Markov decision processes discrete-time multi-stage optimization networks stochastic optimal control Markov renewal programs Bayesian control models partially observable processes

Authors and affiliations

  • Karl Hinderer
    • 1
  • Ulrich Rieder
    • 2
  • Michael Stieglitz
    • 3
  1. 1.Karlsruher Institut für Technologie (KIT)KarlsruheGermany
  2. 2.University of UlmUlmGermany
  3. 3.Karlsruher Institut für Technologie (KIT)KarlsruheGermany

Bibliographic information

  • 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
  • Print ISBN 978-3-319-48813-4
  • Online ISBN 978-3-319-48814-1
  • Series Print ISSN 0172-5939
  • Series Online ISSN 2191-6675
  • About this book