Numerical Methods for Stochastic Control Problems in Continuous Time

Kushner, Harold J. and Paul G. Dupuis:

ISBN 10: 0387978348 / ISBN 13: 9780387978345

Published by New York / Berlin / Heidelberg, Springer-Verlag, 1992

About this Item

IX 439 Seiten Ausgetragenes Bibliotheksexemplar mit den üblichen Inventarvermerken und Stempeln. Bibliothekskarte auf Vorsatz. Biliotheksschild auf Rücken und hinterem Deckel. Markierung auf Fußschnitt. Einband berieben. Abdruck einer Büroklammer auf Schmutztitel. Erste Seiten knickspurig (bis S. 2). Papier ansonsten fast neuwertig. Buch ist allgemein in einem sehr guten Zustand. Zahlr. Abb. // Ex-library book with the usual stamps and registration numbers. Impression of a paper clip on half-title. General condition: very good. MIG-25-06B Sprache: Englisch Gewicht in Gramm: 788.

Seller Inventory # 1845339

Bibliographic Details

• Title: Numerical Methods for Stochastic Control Problems in Continuous Time
• Publisher: New York / Berlin / Heidelberg, Springer-Verlag
• Publication Date: 1992
• Language: English
• ISBN 10: 0387978348
• ISBN 13: 9780387978345
• Binding: 8° Gebundene Ausgabe
• Edition: Applications of Mathematics Vol. 24.
• Condition: Sehr gut
• Weight: 788 grams
• Seller catalogs:
  • Mathematik

About this title

Synopsis

The book presents a comprehensive development of effective numerical methods for stochastic control problems in continuous time. The process models are diffusions, jump-diffusions or reflected diffusions of the type that occur in the majority of current applications. All the usual problem formulations are included, as well as those of more recent interest such as ergodic control, singular control and the types of reflected diffusions used as models of queuing networks. Convergence of the numerical approximations is proved via the efficient probabilistic methods of weak convergence theory. The methods also apply to the calculation of functionals of uncontrolled processes and for the appropriate to optimal nonlinear filters as well. Applications to complex deterministic problems are illustrated via application to a large class of problems from the calculus of variations. The general approach is known as the Markov Chain Approximation Method. Essentially all that is required of the approximations are some natural local consistency conditions. The approximations are consistent with standard methods of numerical analysis. The required background in stochastic processes is surveyed, there is an extensive development of methods of approximation, and a chapter is devoted to computational techniques. The book is written on two levels, that of practice (algorithms and applications), and that of the mathematical development. Thus the methods and use should be broadly accessible.

About the Author

Harold J. Kushner is Professor of Applied Mathematics and Engineering at Brown University and is one of the leading researchers in the area of stochastic processes concerned with analysis and synthesis in control and communications theory.