Reducing measurements for Kiefer-Wolfowitz Algorithm

Abstract

A Kiefer-Wolfowitz algorithm that uses randomized differences and truncations at randomly varying bounds is given in this paper. At each iteration of the algorithm only two observations are required in contrast to 2l observations, where l is the dimension, in the classical algorithm. The algorithm given here is shown to be convergent under only some mild conditions.