Discrete-Time Markov Control Processes: Basic Optimality Criteria (Applications of Mathematics, Volume 30)

Onesimo Hernandez-Lerma et Jean Bernard Lasserre

ISBN 10: 0387945792 / ISBN 13: 9780387945798

Published by Springer, 1996

About this Item

Ancien livre de bibliothèque avec équipements. Edition 1996. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Edition 1996. Ammareal gives back up to 15% of this item's net price to charity organizations.

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Bibliographic Details

• Title: Discrete-Time Markov Control Processes: Basic Optimality Criteria (Applications of Mathematics, Volume 30)
• Publisher: Springer
• Publication Date: 1996
• Language: English
• ISBN 10: 0387945792
• ISBN 13: 9780387945798
• Binding: Hardcover
• Condition: Très bon

About this title

Synopsis

This book presents the first part of a planned two-volume series devoted to a systematic exposition of some recent developments in the theory of discrete-time Markov control processes (MCPs). Interest is mainly confined to MCPs with Borel state and control (or action) spaces, and possibly unbounded costs and noncompact control constraint sets. MCPs are a class of stochastic control problems, also known as Markov decision processes, controlled Markov processes, or stochastic dynamic pro­ grams; sometimes, particularly when the state space is a countable set, they are also called Markov decision (or controlled Markov) chains. Regardless of the name used, MCPs appear in many fields, for example, engineering, economics, operations research, statistics, renewable and nonrenewable re­ source management, (control of) epidemics, etc. However, most of the lit­ erature (say, at least 90%) is concentrated on MCPs for which (a) the state space is a countable set, and/or (b) the costs-per-stage are bounded, and/or (c) the control constraint sets are compact. But curiously enough, the most widely used control model in engineering and economics--namely the LQ (Linear system/Quadratic cost) model-satisfies none of these conditions. Moreover, when dealing with "partially observable" systems) a standard approach is to transform them into equivalent "completely observable" sys­ tems in a larger state space (in fact, a space of probability measures), which is uncountable even if the original state process is finite-valued.

From the Back Cover

This book provides a unified, comprehensive treatment of some recent theoretical developments on Markov control processes. Interest is mainly confined to MPCs with Borel state and control spaces, and possibly unbounded costs and noncompact control constraint sets. The control model studied is sufficiently general to include virtually all of the usual discrete-time stochastic control models that appear in applications to engineering, economics, mathematical population processes, operations research, and management science. Much of the material appears for the first time in book form.