DocumentCode
614632
Title
Formal analysis for practical gain sequence selection in recursive stochastic approximation algorithms
Author
Qi Wang
Author_Institution
Dept. of Appl. Math. & Stat., Johns Hopkins Univ., Baltimore, MD, USA
fYear
2013
fDate
20-22 March 2013
Firstpage
1
Lastpage
6
Abstract
For many popular stochastic approximation algorithms, such as simultaneous perturbation stochastic approximation method and stochastic gradient method, the practical gain sequence selections are different from the optimal selection, which is theoretically derived from asymptotically performance. We provide formal justification for the reasons why we choose such gain sequence in practice.
Keywords
approximation theory; gradient methods; recursive estimation; sequences; stochastic processes; formal analysis; perturbation stochastic approximation method; practical gain sequence selection; recursive estimation; recursive stochastic approximation algorithm; stochastic gradient method; Antennas; Bandwidth; Energy storage; Impedance; Q-factor; RLC circuits; Zinc; Practical Gain Sequences; Recursive Estimation; Stochastic Approximation;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Sciences and Systems (CISS), 2013 47th Annual Conference on
Conference_Location
Baltimore, MD
Print_ISBN
978-1-4673-5237-6
Electronic_ISBN
978-1-4673-5238-3
Type
conf
DOI
10.1109/CISS.2013.6552320
Filename
6552320
Link To Document