Title :
A stopping rule for linear stochastic approximation
Author :
Wada, Takayuki ; Itani, Takamitsu ; Fujisaki, Yasumasa
Author_Institution :
Dept. of Syst. Sci., Kobe Univ., Nada, Japan
Abstract :
A stopping rule is developed for multidimensional stochastic approximation which seeks for a solution of an unknown equation based on random noise corrupted residuals. It is assumed that the equation is linear, and the noise is independent and identically distributed random vectors with a bounded covariance. Then, it is shown that the necessary number of iterations is bounded by a polynomial of the covariance and parameters which specify probabilistic precision on the resultant candidate of the solution.
Keywords :
approximation theory; polynomials; random noise; stochastic processes; vectors; bounded covariance; covariance polynomial; distributed random vectors; linear stochastic approximation; random noise; stopping rule; Approximation algorithms; Approximation methods; Convergence; Equations; Estimation error; Noise; Probabilistic logic;
Conference_Titel :
Decision and Control (CDC), 2010 49th IEEE Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4244-7745-6
DOI :
10.1109/CDC.2010.5717389
