Stochastic Approximation and Recursive Algorithms and Applications (inbunden)
Inbunden (Hardback)
Antal sidor
2nd ed. 2003
Springer-Verlag New York Inc.
Yin, George G.
31 Abb
9 Tables, black and white; XXII, 478 p.
v. 35
240 x 160 x 30 mm
840 g
Antal komponenter
1 Hardback
Stochastic Approximation and Recursive Algorithms and Applications (inbunden)

Stochastic Approximation and Recursive Algorithms and Applications

Inbunden Engelska, 2003-07-01
Skickas inom 7-10 vardagar.
Fri frakt inom Sverige för privatpersoner.
Finns även som
Visa alla 2 format & utgåvor
This book presents a thorough development of the modern theory of stochastic approximation or recursive stochastic algorithms for both constrained and unconstrained problems. This second edition is a thorough revision, although the main features and structure remain unchanged. It contains many additional applications and results as well as more detailed discussion.
Visa hela texten

Passar bra ihop

  1. Stochastic Approximation and Recursive Algorithms and Applications
  2. +
  3. System Identification with Quantized Observations

De som köpt den här boken har ofta också köpt System Identification with Quantized Observations av Le Yi Wang, G George Yin, Jifeng Zhang, Yanlong Zhao (inbunden).

Köp båda 2 för 2968 kr


Har du läst boken? Sätt ditt betyg »

Recensioner i media

From the reviews of the second edition: "This is the second edition of an excellent book on stochastic approximation, recursive algorithms and applications ... . Although the structure of the book has not been changed, the authors have thoroughly revised it and added additional material ... ." (Evelyn Buckwar, Zentralblatt MATH, Vol. 1026, 2004) "The book attempts to convince that ... algorithms naturally arise in many application areas ... . I do not hesitate to conclude that this book is exceptionally well written. The literature citation is extensive, and pertinent to the topics at hand, throughout. This book could be well suited to those at the level of the graduate researcher and upwards." (A. C. Brooms, Journal of the Royal Statistical Society Series A: Statistics in Society, Vol. 169 (3), 2006)

Bloggat om Stochastic Approximation and Recursive Al...


Introduction 1 Review of Continuous Time Models 1.1 Martingales and Martingale Inequalities 1.2 Stochastic Integration 1.3 Stochastic Differential Equations: Diffusions 1.4 Reflected Diffusions 1.5 Processes with Jumps 2 Controlled Markov Chains 2.1 Recursive Equations for the Cost 2.2 Optimal Stopping Problems 2.3 Discounted Cost 2.4 Control to a Target Set and Contraction Mappings 2.5 Finite Time Control Problems 3 Dynamic Programming Equations 3.1 Functionals of Uncontrolled Processes 3.2 The Optimal Stopping Problem 3.3 Control Until a Target Set Is Reached 3.4 A Discounted Problem with a Target Set and Reflection 3.5 Average Cost Per Unit Time 4 Markov Chain Approximation Method: Introduction 4.1 Markov Chain Approximation 4.2 Continuous Time Interpolation 4.3 A Markov Chain Interpolation 4.4 A Random Walk Approximation 4.5 A Deterministic Discounted Problem 4.6 Deterministic Relaxed Controls 5 Construction of the Approximating Markov Chains 5.1 One Dimensional Examples 5.2 Numerical Simplifications 5.3 The General Finite Difference Method 5.4 A Direct Construction 5.5 Variable Grids 5.6 Jump Diffusion Processes 5.7 Reflecting Boundaries 5.8 Dynamic Programming Equations 5.9 Controlled and State Dependent Variance 6 Computational Methods for Controlled Markov Chains 6.1 The Problem Formulation 6.2 Classical Iterative Methods 6.3 Error Bounds 6.4 Accelerated Jacobi and Gauss-Seidel Methods 6.5 Domain Decomposition 6.6 Coarse Grid-Fine Grid Solutions 6.7 A Multigrid Method 6.8 Linear Programming 7 The Ergodic Cost Problem: Formulation and Algorithms 7.1 Formulation of the Control Problem 7.2 A Jacobi Type Iteration 7.3 Approximation in Policy Space 7.4 Numerical Methods 7.5 The Control Problem 7.6 The Interpolated Process 7.7 Computations 7.8 Boundary Costs and Controls 8 Heavy Traffic and Singular Control 8.1 Motivating Examples &nb