Accession Number : AD0738965

Title :   Stochastic Gradient Algorithms for Searching Multidimensional Multimodal Surfaces.

Descriptive Note : Technical rept.,

Corporate Author : STANFORD UNIV CALIF STANFORD ELECTRONICS LABS

Personal Author(s) : Gucker,George R.

Report Date : OCT 1969

Pagination or Media Count : 127

Abstract : Certain optimization problems can be reduced to the form: given a criterion function of (vector W) dependent upon a set of scalar adjustments acting as components of a vector (vector W) find a vector (vector W*) such that h(vector W*) <or= h (vector W) for all admissible (vector W). It is often helpful to consider h(vector W) a surface defined on a multidimensional vector space. The stochastic gradient approach to the problem of searching a general multidimensional surface for a minimum is based upon the fact that an unbiased gradient estimate for a steepest descent algorithm can be obtained easily from a measurement of the surface at a random displacement of the basepoint. Unlike other steepest descent methods, this method does not necessarily impose the restriction that the perturbation be kept small to obtain an accurate gradient estimate. (Author)

Descriptors :   (*SEARCH THEORY, *OPTIMIZATION), (*STEEPEST DESCENT METHOD, STOCHASTIC PROCESSES), VECTOR SPACES, FUNCTIONAL ANALYSIS, RANDOM VARIABLES, SURFACES, PROBABILITY DENSITY FUNCTIONS, MATRICES(MATHEMATICS), STATISTICAL PROCESSES, ALGORITHMS

Subject Categories : Operations Research

Distribution Statement : APPROVED FOR PUBLIC RELEASE