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Approximate Iterative Algorithms

Anthony Louis Almudevar
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advanced iterative algorithm design
Algorithm Tolerance
Approximate Iterative Algorithms
Author_Anthony Louis Almudevar
Average Cost
banach
Banach Fixed Point Theorem
Banach Space
borel
Borel Space
Bounded Linear Operator
Category=PBKS
Category=PBW
chains
convergence analysis
dynamic programming techniques
Embedded Markov Chain
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Fixed Point Equation
functional analysis methods
kernel
Linear Operator
lipschitz
Lipschitz Constant
Lipschitz Continuous
markov
Markov Decision Processes
MDP
Metric Space
norm
Normed Vector Space
numerical optimisation
Operator Tolerance
probability theory applications
Queueing System
Quotient Space
Ra Te
Real Valued Function
Relative Error Model
RMA
space
stochastic
Stochastic Kernel
stochastic processes
supremum
Supremum Norm

Product details

  • ISBN 9780415621540
  • Weight: 824g
  • Dimensions: 174 x 246mm
  • Publication Date: 18 Feb 2014
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Description

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.
About Anthony Louis Almudevar
Dr. Almudevar was born in Halifax and raised in Ontario, Canada. He completed a PhD in Statistics at the University of Toronto, and is currently a faculty member in the Department of Biostatistics and Computational Biology at the University of Rochester. He has a wide range of interests, which include biological network modeling, analysis of genetic data, immunological modeling and clinical applications of technological home monitoring. He has a more general interest in optimization and control theory, with an emphasis on the computational issues associated with Markov decision processes.

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