ABSTRACT

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

chapter 1|2 pages

Introduction

part |2 pages

PART I Mathematical background

chapter 2|22 pages

Real analysis and linear algebra

chapter 3|24 pages

Background – measure theory

chapter 4|46 pages

Background – probability theory

chapter 5|28 pages

Background – stochastic processes

chapter 6|32 pages

Functional analysis

chapter 7|16 pages

Fixed point equations

chapter 8|16 pages

The distribution of a maximum

part |2 pages

PART II General theory of approximate iterative algorithms

part |2 pages

PART III: Application to Markov decision processes