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
TABLE OF CONTENTS
part |2 pages
PART I Mathematical background
part |2 pages
PART II General theory of approximate iterative algorithms
part |2 pages
PART III: Application to Markov decision processes