Skip to main content

Computational Methods for Controlled Markov Chains

  • Chapter
  • 1926 Accesses

Part of the book series: Stochastic Modelling and Applied Probability ((SMAP,volume 24))

Abstract

The chapter presents many of the basic ideas which are in current use for the solution of the dynamic programming equations for the optimal control and value function for the approximating Markov chain models. We concentrate on methods for problems which are of interest over a potentially unbounded time interval. Numerical methods for the ergodic problem will be discussed in Chapter 7, and are simple modifications of the ideas of this chapter. Some approaches to the numerical problem for the finite time problem will be discussed in Chapter 12.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   79.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer Science+Business Media New York

About this chapter

Cite this chapter

Kushner, H.J., Dupuis, P. (2001). Computational Methods for Controlled Markov Chains. In: Numerical Methods for Stochastic Control Problems in Continuous Time. Stochastic Modelling and Applied Probability, vol 24. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-0007-6_7

Download citation

  • DOI: https://doi.org/10.1007/978-1-4613-0007-6_7

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4612-6531-3

  • Online ISBN: 978-1-4613-0007-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics