Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
711179 | IFAC-PapersOnLine | 2015 | 5 Pages |
Abstract
Smoothed functional gradient algorithm with perturbations distributed according to the Gaussian distribution is considered for stochastic optimization problem with additive noise. A stochastic approximation algorithm with expanding truncations that uses either one-sided or two-sided gradient estimate is given. At each iteration of the algorithm only two observations are required. The algorithm is shown to be convergent under only some mild conditions
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