DocumentCode
2198623
Title
Law of the iterated logarithm for a constant-gain linear stochastic gradient algorithm
Author
Joslin, Jeff A. ; Heunis, Andrew J.
Author_Institution
Dept. of Electr. & Comput. Eng., Waterloo Univ., Ont., Canada
Volume
5
fYear
2001
fDate
2001
Firstpage
5044
Abstract
We characterize the finite-horizon limiting properties of a constant-gain linear stochastic gradient algorithm, as the adaptation gain tends to zero, in the form of a functional law of the iterated logarithm
Keywords
Gaussian processes; Markov processes; gradient methods; iterative methods; probability; Gauss-Markov process; interpolation; iterated logarithm; probability; stochastic gradient algorithm; Differential equations; Gaussian processes; Interpolation; Joining processes; Random sequences; Random variables; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 2001. Proceedings of the 40th IEEE Conference on
Conference_Location
Orlando, FL
Print_ISBN
0-7803-7061-9
Type
conf
DOI
10.1109/.2001.981010
Filename
981010
Link To Document