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Approximate Iterative Algorithms

By: (Author) Anthony Louis Almudevar

Werezi Extended Catalogue
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Ksh 20,700.00

Format: Hardback or Cased Book

ISBN-10: 0415621542

ISBN-13: 9780415621540

Publisher: Taylor & Francis Ltd

Imprint: CRC Press

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Feb 18th, 2014

Print length: 372 Pages

Weight: 794 grams

Dimensions (height x width x thickness): 25.30 x 17.20 x 2.60 cms

Product Classification: Numerical analysis
Applied mathematics

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Iterative algorithms often rely on approximate evaluation techniques, which may include statistical estimation, computer simulation or functional approximation. This volume presents methods for the study of approximate iterative algorithms, providing tools for the derivation of error bounds and convergence rates, and for the optimal design of such algorithms. Techniques of functional analysis are used to derive analytical relationships between approximation methods and convergence properties for general classes of algorithms. The volume provides the necessary background in functional analysis and probability theory and presents extensive applications to Markov decision processes.

Iterative algorithms often rely on approximate evaluation techniques, which may include statistical estimation, computer simulation or functional approximation. This volume presents methods for the study of approximate iterative algorithms, providing tools for the derivation of error bounds and convergence rates, and for the optimal design of such algorithms. Techniques of functional analysis are used to derive analytical relationships between approximation methods and convergence properties for general classes of algorithms. This work provides the necessary background in functional analysis and probability theory. Extensive applications to Markov decision processes are presented.



This volume is intended for mathematicians, engineers and computer scientists, who work on learning processes in numerical analysis and are involved with optimization, optimal control, decision analysis and machine learning.


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