Anthony Louis Almudevar

Approximate Iterative Algorithms

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Routledge
• Seitenzahl: 372
• Erscheinungstermin: 18. Februar 2014
• Englisch
• Abmessung: 254mm x 175mm x 33mm
• Gewicht: 1225g
• ISBN-13: 9780415621540
• ISBN-10: 0415621542
• Artikelnr.: 36964687

Autorenporträt

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.

Inhaltsangabe

1. Introduction. PART I Mathematical background: 2. Real analysis and
linear algebra 3. Background - measure theory 4. Background - probability
theory 5. Background - stochastic processes 6. Functional analysis 7. Fixed
point equations 8. The distribution of a maximum. PART II General theory of
approximate iterative algorithms: 9. Background - linear convergence 10. A
general theory of approximate iterative algorithms (AIA) 11. Selection of
approximation schedules for coarse-to-fine AIAs. PART III Application to
Markov decision processes: 12. Markov decision processes (MDP) - background
13. Markov decision processes - value iteration 14. Model approximation in
dynamic programming - general theory 15. Sampling based approximation
methods 16. Approximate value iteration by truncation 17. Grid
approximations of MDPs with continuous state/action spaces 18. Adaptive
control of MDPs.