Name
Title | ||
|---|---|---|
Optimization via simulation: randomized-direction stochastic approximation algorithms using deterministic sequences |
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 a.s. 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. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.5555/1030453.1030496 | Winter Simulation Conference |
Keywords | Field | DocType |
sufficient condition,stochastic approximation algorithm,two-simulation method,special structure,deterministic perturbation sequence,noise sequence,generalized form,asymptotic normality,deterministic sequence | Convergence (routing),Stochastic optimization,Algorithm,Stochastic approximation,Mathematics,Perturbation (astronomy),Asymptotic distribution | Conference |
ISBN | Citations | PageRank |
0-7803-7615-3 | 3 | 0.55 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoping Xiong | 1 | 31 | 3.37 |
| I-Jeng Wang | 2 | 277 | 31.46 |
| Michael C. Fu | 3 | 1161 | 128.16 |