Discrete-Time Markov Control Processes: Basic Optimality Criteria: 30 (Stochastic Modelling and Applied Probability) - Softcover

Hernandez-Lerma, Onesimo; Lasserre, Jean B.

 
9781461268840: Discrete-Time Markov Control Processes: Basic Optimality Criteria: 30 (Stochastic Modelling and Applied Probability)
Softcover
ISBN 10: 1461268842 ISBN 13: 9781461268840
Verlag: Springer, 2012

Inhaltsangabe

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.

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Reseña del editor

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.

Reseña del editor

This book provides a unified, comprehensive treatment of some recent theoretical developments on Markov control processes. Interest is mainly confined to MCPs with Borel state and control spaces, and possibly unbounded costs and non-compact control constraint sets. The control model studied is sufficiently general to include virtually all 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.

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Verlag
Springer
Erscheinungsdatum
2012
Sprache
Englisch
ISBN 10 
1461268842
ISBN 13 
9781461268840
Einband
Tapa blanda
Anzahl der Seiten
236
Kontakt zum Hersteller
Nicht verfügbar
Verantwortliche Person
CMN Printpool Inh. Mathis Neumann
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Hamburg
Deutschland

Weitere beliebte Ausgaben desselben Titels

9780387945798: Discrete-Time Markov Control Processes: Basic Optimality Criteria: 30 (Stochastic Modelling and Applied Probability)

Vorgestellte Ausgabe

ISBN 10:  0387945792 ISBN 13:  9780387945798
Verlag: Springer, 1995
Hardcover