The book deals with a powerful and convenient approach to a great variety of types of problems of the recursive monte-carlo or stochastic approximation type. The approach, relating algorithm behavior to qualitative properties of deterministic or stochastic differ ential equations, has advantages in algorithm conceptualiza tion and design.
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I. Introduction.- 1.1. General Remarks.- 1.2. The Robbins-Monro Process.- 1.3. A “Continuous” Process Version of Section 2.- 1.4. Regulation of a Dynamical System; a simple example.- 1.5. Function Minimization: The Kiefer-Wolfowitz Procedure.- 1.6. Constrained Problems.- 1.7. An Economics Example.- II. Convergence w.p.1 for Unconstrained Systems.- 2.1. Preliminaries and Motivation.- 2.2. The Robbins-Monro and Kiefer-Wolfowitz Algorithms: Conditions and Discussion.- 2.3. Convergence Proofs for RM and KW-like Procedures.- 2.3.1. A Basic RM-like Procedure.- 2.3.2. One Dimensional RM and Accelerated RM Procedures.- 2.3.3. A Continuous Parameter RM Procedure.- 2.3.4. The Basic Kiefer-Wolfowitz Procedure.- 2.3.5. Random Directions KW Methods.- 2.4. A General Robbins-Monro Process: “Exogenous Noise”.- 2.4.1. The Case of Bounded h(•,•).- 2.4.2. Unbounded h(•,•): Exogenous Noise.- 2.5. A General RM Process; State Dependent Noise.- 2.5.1. Extensions and Localizations of Theorem 2.5.2.- 2.6. Some Applications.- 2.7. Mensov-Rademacher Estimates.- III. Weak Convergence of Probability Measures.- IV. Weak Convergence for Unconstrained Systems.- 4.1. Conditions and General Discussion.- 4.2. The Robbins-Monro and Kiefer-Wolfowitz Procedures.- 4.2.1. The Basic Robbins-Monro Procedure.- 4.2.2. The One-Dimensional Robbins-Monro Procedure.- 4.2.3. The Kiefer-Wolfowitz Procedure.- 4.2.4. A Case Where the Limit Satisfies a Generalized ODE.- 4.2.5. A Continuous Parameter KW Procedure.- 4.3. A General Robbins-Monro Process: Exogenous Noise.- 4.4. A General RM Process: State Dependent Noise.- 4.5. The Identification Problem.- 4.6. A Counter-Example to Tightness.- 4.7. Boundedness of {Xn} and Tightness of {Xn(•)}.- V. Convergence w.p.1 For Constrained Systems.- 5.1. A Penalty-Multiplier Algorithm for Equality Constraints.- 5.1.1. A Basic RM-like Algorithm, Conditions and Discussion.- 5.1.2. The Noise Condition, Discussion and Generalization.- 5.1.3. Boundedness of {Xn}.- 5.1.4. Proof of the Main Theorem.- 5.1.5. Constrained Function Minimization and Other Extensions.- 5.2. A Lagrangian Method for Inequality Constraints.- 5.2.1. The Algorithm and Conditions.- 5.2.2. The Convergence Theorem 18.- 5.2.3. A Non-Convergent but Useful Algorithm.- 5.2.4. An Application to the Identification Problem.- 5.3. A Projection Algorithm.- 5.4. A Penalty-Multiplier Method for Inequality Constraints.- VI. Weak Convergence: Constrained Systems.- 6.1. A Multiplier Type Algorithm for Equality Constraints.- 6.1.1. Boundedness of {Xn}.- 6.1.2. The Noise Condition, Discussion.- 6.1.3. The Convergence Theorem.- 6.2. The Lagrangian Method.- 6.3. A Projection Algorithm.- 6.4. A Penalty-Multiplier Algorithm for Inequality Constraints.- VII. Rates of Convergence.- 7.1. The Problem Formulation.- 7.2. Conditions and Discussions.- 7.3. Rates of Convergence for Case 1, the KW Algorithm.- 7.4. Discussion of Rates of Convergence for Two KW Algorithms.
Book by Harold J Kushner Dean S Clark
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Softcover. Condizione: Bon. Ancien livre de bibliothèque. Traces d'usure sur la couverture. Petite(s) trace(s) de pliure sur la couverture. Couverture légèrement déchirée. Salissures sur la tranche. Edition 1978. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisa ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. Signs of wear on the cover. Slightly creased cover. Slightly torn cover. Stains on the edge. Edition 1978. Ammareal gives back up to 15% of this item's net price to charity organizations. Codice articolo D-597-600
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The book deals with a powerful and convenient approach to a great variety of types of problems of the recursive monte-carlo or stochastic approximation type. Such recu- sive algorithms occur frequently in stochastic and adaptive control and optimization the. Codice articolo 5911657
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The book deals with a powerful and convenient approach to a great variety of types of problems of the recursive monte-carlo or stochastic approximation type. Such recu- sive algorithms occur frequently in stochastic and adaptive control and optimization theory and in statistical esti- tion theory. Typically, a sequence {X } of estimates of a n parameter is obtained by means of some recursive statistical th st procedure. The n estimate is some function of the n_l estimate and of some new observational data, and the aim is to study the convergence, rate of convergence, and the pa- metric dependence and other qualitative properties of the - gorithms. In this sense, the theory is a statistical version of recursive numerical analysis. The approach taken involves the use of relatively simple compactness methods. Most standard results for Kiefer-Wolfowitz and Robbins-Monro like methods are extended considerably. Constrained and unconstrained problems are treated, as is the rate of convergence problem. While the basic method is rather simple, it can be elaborated to allow a broad and deep coverage of stochastic approximation like problems. The approach, relating algorithm behavior to qualitative properties of deterministic or stochastic differ ential equations, has advantages in algorithm conceptualiza tion and design. It is often possible to obtain an intuitive understanding of algorithm behavior or qualitative dependence upon parameters, etc., without getting involved in a great deal of deta~l. 276 pp. Englisch. Codice articolo 9780387903415
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The book deals with a powerful and convenient approach to a great variety of types of problems of the recursive monte-carlo or stochastic approximation type. Such recu- sive algorithms occur frequently in stochastic and adaptive control and optimization theory and in statistical esti- tion theory. Typically, a sequence {X } of estimates of a n parameter is obtained by means of some recursive statistical th st procedure. The n estimate is some function of the n_l estimate and of some new observational data, and the aim is to study the convergence, rate of convergence, and the pa- metric dependence and other qualitative properties of the - gorithms. In this sense, the theory is a statistical version of recursive numerical analysis. The approach taken involves the use of relatively simple compactness methods. Most standard results for Kiefer-Wolfowitz and Robbins-Monro like methods are extended considerably. Constrained and unconstrained problems are treated, as is the rate of convergence problem. While the basic method is rather simple, it can be elaborated to allow a broad and deep coverage of stochastic approximation like problems. The approach, relating algorithm behavior to qualitative properties of deterministic or stochastic differ ential equations, has advantages in algorithm conceptualiza tion and design. It is often possible to obtain an intuitive understanding of algorithm behavior or qualitative dependence upon parameters, etc., without getting involved in a great deal of deta~l.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 276 pp. Englisch. Codice articolo 9780387903415
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