Title :
Randomized-direction stochastic approximation algorithms using deterministic sequences
Author :
Xiaoping Xiong ; I-Jeng Wang
Author_Institution :
R.H. Smith Sch. of Bus., Maryland Univ., USA
Abstract :
We study the convergence and asymptotic normality of a generalized form of stochastic approximation algorithm with deterministic perturbation sequences. Both one-simulation and two-simulation methods are considered. Assuming a special structure of deterministic sequence, we establish sufficient condition on the noise sequence for AS convergence of the algorithm. Construction of such a special structure of deterministic sequence follows the discussion of asymptotic normality. Finally we discuss ideas on further research in analysis and design of the deterministic perturbation sequences.
Keywords :
convergence of numerical methods; randomised algorithms; sequences; simulation; stochastic processes; asymptotic normality; convergence; deterministic perturbation sequences; noise sequence; one-simulation methods; randomized-direction stochastic approximation algorithms; two-simulation methods; Approximation algorithms; Computational modeling; Convergence; Design optimization; Educational institutions; Laboratories; Physics; Stochastic processes; Stochastic resonance; Sufficient conditions;
Conference_Titel :
Simulation Conference, 2002. Proceedings of the Winter
Print_ISBN :
0-7803-7614-5
DOI :
10.1109/WSC.2002.1172897
