Numerical methods for controlled stochastic delay systems

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1. Verfasser: Kushner, Harold J. 1933- (VerfasserIn)
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Sprache:English
Veröffentlicht: Boston [u.a.] Birkhäuser 2008
Schriftenreihe:Systems & control: foundations & applications
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adam_text Contents Preface . xi Examples and Introduction . 1 1.0 Outline of the Chapter . 1 1.1 An Introductory Example: Controlling the Temperature of a Fluid Flow . 1 1.2 An Example from Internet Regulation . 5 1.3 Models With Finite-Dimensional Equivalences . 10 Weak Convergence and Martingales . 13 2.0 Outline of the Chapter . 13 2.1 Weak Convergence . 14 2.1.1 Basic Theorems of Weak Convergence . 15 2.1.2 The Function Spaces D(S; I) . 17 2.2 Martingales and the Martingale Method . 18 2.2.1 Martingales . 18 2.2.2 Verifying That a Process Is a Martingale . 20 Stochastic Delay Equations: Models . 23 3.0 Outline of the Chapter . 23 3.1 The System Model: Boundary Absorption . 25 3.2 Reflecting Diffusions . 30 3.2.1 The Reflected Diffusion . 30 3.2.2 Delayed Control, Reflection Term, and/or Wiener Process . 35 3.2.3 Neutral Equations . 37 3.2.4 Controlled Variance and Jumps . 37 3.3 The Girsanov Transformation . 38 3.4 Cost Functions . 40 3.5 Existence of an Optimal Control . 43 Contents 3.5.1 Reflecting or Absorbing Boundary: Discounted Cost . 43 3.6 Singular and Impulsive Controls . 50 3.6.1 Singular Controls . 50 3.6.2 Definition of and Existence of Solutions . 51 3.6.3 Existence of an Optimal Control . 56 3.6.4 Impulsive Controls . 58 Approximations to the Dynamical Models . 61 4.0 Outline of the Chapter . 61 4.1 Approximations of the Dynamical Systems . 63 4.1.1 A Basic Approximation . 63 4.2 Approximations by Time-Varying Delays . 69 4.2.1 Discretized Delays . 69 4.2.2 Periodic Delays . 71 4.2.3 Randomly Varying Delays . 73 4.2.4 Periodic-Erlang Delays . 75 4.2.5 Convergence of Costs and Existence of Optimal Controls 77 4.2.6 Differential Operator for the Periodic-Erlang Approximation . 78 4.3 Simulations Illustrating the Model Approximations . 78 4.3.1 Simulations Based on the Periodic Approximation . 78 4.3.2 Simulations Based on the Periodic-Erlang Approximation 82 4.4 Approximations: Path and Control Delayed . 84 4.5 Singular Controls . 90 4.6 Rapidly Varying Delays . 92 The Ergodic Cost Problem . 97 5.0 Outline of the Chapter . 97 5.1 The Basic Model . 98 5.1.1 Relaxed Feedback Controls . 98 5.1.2 Density Properties and Preliminary Results .101 5.2 The Doeblin Condition .105 5.3 Approximations of the Models .107 5.4 Approximations with Periodic Delays .110 5.4.1 Limit and Approximation Results for Periodic Delays . . 110 5.4.2 Smoothed Nearly Optimal Controls .115 5.4.3 Delays in the Variance Term .117 5.5 The Periodic-Erlang Approximation .118 Markov Chain Approximations: Introduction .125 6.0 Outline of the Chapter .125 6.1 The System Model .126 6.2 Approximating Chains and Local Consistency .127 6.3 Continuous-Time Interpolations .131 6.3.1 The Continuous-Time Interpolation ξΗ(·) .131 Contents ix 6.3.2 A Markov Continuous-Time Interpolation .133 6.4 The "Explicit" Approximation Procedure .137 6.5 The "Implicit" Approximating Processes .140 6.5.1 The General Implicit Approximation Method .142 6.5.2 Continuous-Time Interpolations .144 6.5.3 Representations of the Cost Function .146 6.5.4 Asymptotic Equivalence of the Timescales .147 6.5.5 Convergence .149 6.6 Singular and Impulsive Controls .149 6.6.1 Singular Controls .149 6.6.2 Impulsive Control .153 6.7 The Ergodic Cost Function .153 6.7.1 Introduction .153 6.7.2 The Markov Chain Approximation Method .154 Markov Chain Approximations: Path and Control Delayed 159 7.0 Outline of the Chapter .159 7.1 The Model and Local Consistency .161 7.1.1 The Models .161 7.1.2 Delay in Path Only: Local Consistency and Interpolations .162 7.1.3 Delay in the Path and Control .167 7.1.4 Absorbing Boundaries and Other Cost Functions .171 7.1.5 Approximations to the Memory Segments .172 7.2 Computational Procedures .175 7.2.1 Delay in the Path Only: State Representations and the Bellman Equation .175 7.2.2 Delay in Both Path and Control .178 7.2.3 A Comment on Higher-Dimensional Problems .179 7.3 The Implicit Numerical Approximation: Path Delayed .180 7.3.1 Local Consistency and the Memory Segment .180 7.3.2 The Cost Function and Bellman Equation .185 7.3.3 The Use of Averaging in Constructing the Path Memory Approximation .186 7.3.4 Timescales .187 7.3.5 Convergence Theorems .188 7.4 The Implicit Approximation Procedure and the Random Delay Model .189 Path and Control Delayed: Continued .193 8.0 Outline of the Chapter .193 8.1 Periodic Approximations to the Delay: Path Delayed .194 8.2 A Periodic-Erlang Model .196 8.3 The Number of Points in the State Space: Path Only Delayed . 200 χ Contents 8.3.1 The Implicit and Periodic- Erlang Approximation Methods: Reduced Memory .200 8.4 Control and Path Delayed .203 8.4.1 A Periodic Approximating Memory Segment .204 8.4.2 A Periodic-Erlang Approximation .207 8.5 Proofs of Convergence .213 8.5.1 Proofs of Theorems from Chapter 7.213 8.5.2 Proof of Theorem 4.1.218 8.6 Singular Controls .219 8.7 Neutral Equations .220 8.8 The Ergodic Cost Problem .222 9 A Wave Equation Approach .227 9.0 Outline of the Chapter .227 9.1 The Model and Assumptions .228 9.2 A Key Representation of x(-) .230 9.2.1 A Representation of the Solution .230 9.2.2 Comments on the Dimension and the System State . 231 9.2.3 Proof of the Representation .232 9.2.4 Extensions .235 9.3 A Discrete-Time Approximation .236 9.4 The Markov Chain Approximation .240 9.4.1 Preliminaries and Boundaries .242 9.4.2 Transition Probabilities and Local Consistency: An Implicit Approximation Procedure .242 9.4.3 Dynamical Representations, the Cost Function and Bellman Equation .248 9.5 Size of the State Space for the Approximating Chain .250 9.6 Proof of Convergence: Preliminaries .252 9.6.1 The Randomization Errors .252 9.6.2 Continuous Time Interpolations .255 9.7 Convergence of the Numerical Algorithm .259 9.8 Alternatives: Periodic and Periodic-Erlang Approximations . . . 261 9.8.1 A Periodic Approximation .261 9.8.2 The Effective Delay and Numerical Procedures .265 9.9 Singular and Impulsive Controls .265 References .267 Index .273 Symbol Index .277 Harold J. Kushner Numerical Methods for Controlled Stochastic Delay Systems The Markov chain approximation methods are widely used for the numerical solution of nonlinear stochastic control problems in continuous time. This book extends the methods to stochastic systems with delays. Because such problems are infinite- dimensional, many new issues arise in getting good numerical approximations and in the convergence proofs. Useful forms of numerical algorithms and system approximations are developed in this work, and the convergence proofs are given. All of the usual cost functions are treated as well as singular and impulsive controls. A major concern is on representations and approximations that use minimal memory. Features and topics include: • Surveys properties of the most important stochastic dynamical models, including singular control, and those for diffusion and reflected diffusion models. • Gives approximations to the dynamical models that simplify the numerical problem, but have only small effects on the behavior. • Develops an ergodic theory for reflected diffusions with delays, as well as model simplifications useful for numerical approximations for average cost per unit time problems. • Provides numerical algorithms for models with delays in the path, or path and control, with reduced memory requirements. • Develops transformations of the problem that yield more efficient approximations when the control, driving Wiener process, and/or reflection processes might be delayed, as well as the path. • Presents examples with applications to control and modern communications systems. The book is the first on the subject and will be of interest to all those who work with stochastic delay equations and whose main interest is in either the use of the algorithms or the underlying mathematics. An excellent resource for graduate students, researchers, and practitioners, the work may be used as a graduate- level textbook for a special topics course or seminar on numerical methods in stochastic control.
adam_txt Contents Preface . xi Examples and Introduction . 1 1.0 Outline of the Chapter . 1 1.1 An Introductory Example: Controlling the Temperature of a Fluid Flow . 1 1.2 An Example from Internet Regulation . 5 1.3 Models With Finite-Dimensional Equivalences . 10 Weak Convergence and Martingales . 13 2.0 Outline of the Chapter . 13 2.1 Weak Convergence . 14 2.1.1 Basic Theorems of Weak Convergence . 15 2.1.2 The Function Spaces D(S; I) . 17 2.2 Martingales and the Martingale Method . 18 2.2.1 Martingales . 18 2.2.2 Verifying That a Process Is a Martingale . 20 Stochastic Delay Equations: Models . 23 3.0 Outline of the Chapter . 23 3.1 The System Model: Boundary Absorption . 25 3.2 Reflecting Diffusions . 30 3.2.1 The Reflected Diffusion . 30 3.2.2 Delayed Control, Reflection Term, and/or Wiener Process . 35 3.2.3 Neutral Equations . 37 3.2.4 Controlled Variance and Jumps . 37 3.3 The Girsanov Transformation . 38 3.4 Cost Functions . 40 3.5 Existence of an Optimal Control . 43 Contents 3.5.1 Reflecting or Absorbing Boundary: Discounted Cost . 43 3.6 Singular and Impulsive Controls . 50 3.6.1 Singular Controls . 50 3.6.2 Definition of and Existence of Solutions . 51 3.6.3 Existence of an Optimal Control . 56 3.6.4 Impulsive Controls . 58 Approximations to the Dynamical Models . 61 4.0 Outline of the Chapter . 61 4.1 Approximations of the Dynamical Systems . 63 4.1.1 A Basic Approximation . 63 4.2 Approximations by Time-Varying Delays . 69 4.2.1 Discretized Delays . 69 4.2.2 Periodic Delays . 71 4.2.3 Randomly Varying Delays . 73 4.2.4 Periodic-Erlang Delays . 75 4.2.5 Convergence of Costs and Existence of Optimal Controls 77 4.2.6 Differential Operator for the Periodic-Erlang Approximation . 78 4.3 Simulations Illustrating the Model Approximations . 78 4.3.1 Simulations Based on the Periodic Approximation . 78 4.3.2 Simulations Based on the Periodic-Erlang Approximation 82 4.4 Approximations: Path and Control Delayed . 84 4.5 Singular Controls . 90 4.6 Rapidly Varying Delays . 92 The Ergodic Cost Problem . 97 5.0 Outline of the Chapter . 97 5.1 The Basic Model . 98 5.1.1 Relaxed Feedback Controls . 98 5.1.2 Density Properties and Preliminary Results .101 5.2 The Doeblin Condition .105 5.3 Approximations of the Models .107 5.4 Approximations with Periodic Delays .110 5.4.1 Limit and Approximation Results for Periodic Delays . . 110 5.4.2 Smoothed Nearly Optimal Controls .115 5.4.3 Delays in the Variance Term .117 5.5 The Periodic-Erlang Approximation .118 Markov Chain Approximations: Introduction .125 6.0 Outline of the Chapter .125 6.1 The System Model .126 6.2 Approximating Chains and Local Consistency .127 6.3 Continuous-Time Interpolations .131 6.3.1 The Continuous-Time Interpolation ξΗ(·) .131 Contents ix 6.3.2 A Markov Continuous-Time Interpolation .133 6.4 The "Explicit" Approximation Procedure .137 6.5 The "Implicit" Approximating Processes .140 6.5.1 The General Implicit Approximation Method .142 6.5.2 Continuous-Time Interpolations .144 6.5.3 Representations of the Cost Function .146 6.5.4 Asymptotic Equivalence of the Timescales .147 6.5.5 Convergence .149 6.6 Singular and Impulsive Controls .149 6.6.1 Singular Controls .149 6.6.2 Impulsive Control .153 6.7 The Ergodic Cost Function .153 6.7.1 Introduction .153 6.7.2 The Markov Chain Approximation Method .154 Markov Chain Approximations: Path and Control Delayed 159 7.0 Outline of the Chapter .159 7.1 The Model and Local Consistency .161 7.1.1 The Models .161 7.1.2 Delay in Path Only: Local Consistency and Interpolations .162 7.1.3 Delay in the Path and Control .167 7.1.4 Absorbing Boundaries and Other Cost Functions .171 7.1.5 Approximations to the Memory Segments .172 7.2 Computational Procedures .175 7.2.1 Delay in the Path Only: State Representations and the Bellman Equation .175 7.2.2 Delay in Both Path and Control .178 7.2.3 A Comment on Higher-Dimensional Problems .179 7.3 The Implicit Numerical Approximation: Path Delayed .180 7.3.1 Local Consistency and the Memory Segment .180 7.3.2 The Cost Function and Bellman Equation .185 7.3.3 The Use of Averaging in Constructing the Path Memory Approximation .186 7.3.4 Timescales .187 7.3.5 Convergence Theorems .188 7.4 The Implicit Approximation Procedure and the Random Delay Model .189 Path and Control Delayed: Continued .193 8.0 Outline of the Chapter .193 8.1 Periodic Approximations to the Delay: Path Delayed .194 8.2 A Periodic-Erlang Model .196 8.3 The Number of Points in the State Space: Path Only Delayed . 200 χ Contents 8.3.1 The Implicit and Periodic- Erlang Approximation Methods: Reduced Memory .200 8.4 Control and Path Delayed .203 8.4.1 A Periodic Approximating Memory Segment .204 8.4.2 A Periodic-Erlang Approximation .207 8.5 Proofs of Convergence .213 8.5.1 Proofs of Theorems from Chapter 7.213 8.5.2 Proof of Theorem 4.1.218 8.6 Singular Controls .219 8.7 Neutral Equations .220 8.8 The Ergodic Cost Problem .222 9 A Wave Equation Approach .227 9.0 Outline of the Chapter .227 9.1 The Model and Assumptions .228 9.2 A Key Representation of x(-) .230 9.2.1 A Representation of the Solution .230 9.2.2 Comments on the Dimension and the System State . 231 9.2.3 Proof of the Representation .232 9.2.4 Extensions .235 9.3 A Discrete-Time Approximation .236 9.4 The Markov Chain Approximation .240 9.4.1 Preliminaries and Boundaries .242 9.4.2 Transition Probabilities and Local Consistency: An Implicit Approximation Procedure .242 9.4.3 Dynamical Representations, the Cost Function and Bellman Equation .248 9.5 Size of the State Space for the Approximating Chain .250 9.6 Proof of Convergence: Preliminaries .252 9.6.1 The Randomization Errors .252 9.6.2 Continuous Time Interpolations .255 9.7 Convergence of the Numerical Algorithm .259 9.8 Alternatives: Periodic and Periodic-Erlang Approximations . . . 261 9.8.1 A Periodic Approximation .261 9.8.2 The Effective Delay and Numerical Procedures .265 9.9 Singular and Impulsive Controls .265 References .267 Index .273 Symbol Index .277 Harold J. Kushner Numerical Methods for Controlled Stochastic Delay Systems The Markov chain approximation methods are widely used for the numerical solution of nonlinear stochastic control problems in continuous time. This book extends the methods to stochastic systems with delays. Because such problems are infinite- dimensional, many new issues arise in getting good numerical approximations and in the convergence proofs. Useful forms of numerical algorithms and system approximations are developed in this work, and the convergence proofs are given. All of the usual cost functions are treated as well as singular and impulsive controls. A major concern is on representations and approximations that use minimal memory. Features and topics include: • Surveys properties of the most important stochastic dynamical models, including singular control, and those for diffusion and reflected diffusion models. • Gives approximations to the dynamical models that simplify the numerical problem, but have only small effects on the behavior. • Develops an ergodic theory for reflected diffusions with delays, as well as model simplifications useful for numerical approximations for average cost per unit time problems. • Provides numerical algorithms for models with delays in the path, or path and control, with reduced memory requirements. • Develops transformations of the problem that yield more efficient approximations when the control, driving Wiener process, and/or reflection processes might be delayed, as well as the path. • Presents examples with applications to control and modern communications systems. The book is the first on the subject and will be of interest to all those who work with stochastic delay equations and whose main interest is in either the use of the algorithms or the underlying mathematics. An excellent resource for graduate students, researchers, and practitioners, the work may be used as a graduate- level textbook for a special topics course or seminar on numerical methods in stochastic control.
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illustrated Illustrated
index_date 2024-09-19T15:27:00Z
indexdate 2024-09-27T16:20:28Z
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isbn 9780817646219
language English
oai_aleph_id oai:aleph.bib-bvb.de:BVB01-016798441
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physical XIX, 281 S. graph. Darst.
publishDate 2008
publishDateSearch 2008
publishDateSort 2008
publisher Birkhäuser
record_format marc
series2 Systems & control: foundations & applications
spellingShingle Kushner, Harold J. 1933-
Numerical methods for controlled stochastic delay systems
Stochastic systems
Markov-Kette (DE-588)4037612-6 gnd
Numerisches Verfahren (DE-588)4128130-5 gnd
Stochastische Kontrolltheorie (DE-588)4263657-7 gnd
Stochastische Differentialgleichung mit Gedächtnis (DE-588)4691382-8 gnd
subject_GND (DE-588)4037612-6
(DE-588)4128130-5
(DE-588)4263657-7
(DE-588)4691382-8
title Numerical methods for controlled stochastic delay systems
title_auth Numerical methods for controlled stochastic delay systems
title_exact_search Numerical methods for controlled stochastic delay systems
title_exact_search_txtP Numerical methods for controlled stochastic delay systems
title_full Numerical methods for controlled stochastic delay systems Harold J. Kushner
title_fullStr Numerical methods for controlled stochastic delay systems Harold J. Kushner
title_full_unstemmed Numerical methods for controlled stochastic delay systems Harold J. Kushner
title_short Numerical methods for controlled stochastic delay systems
title_sort numerical methods for controlled stochastic delay systems
topic Stochastic systems
Markov-Kette (DE-588)4037612-6 gnd
Numerisches Verfahren (DE-588)4128130-5 gnd
Stochastische Kontrolltheorie (DE-588)4263657-7 gnd
Stochastische Differentialgleichung mit Gedächtnis (DE-588)4691382-8 gnd
topic_facet Stochastic systems
Markov-Kette
Numerisches Verfahren
Stochastische Kontrolltheorie
Stochastische Differentialgleichung mit Gedächtnis
url http://deposit.dnb.de/cgi-bin/dokserv?id=2857142&prov=M&dok_var=1&dok_ext=htm
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