A combined direction stochastic approximation algorithm |
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Authors: | Zi Xu |
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Institution: | 1.Department of Mathematics, Science of College,Shanghai University,Shanghai,People’s Republic of China |
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Abstract: | The stochastic approximation problem is to find some root or minimum of a nonlinear function in the presence of noisy measurements.
The classical algorithm for stochastic approximation problem is the Robbins-Monro (RM) algorithm, which uses the noisy negative
gradient direction as the iterative direction. In order to accelerate the classical RM algorithm, this paper gives a new combined
direction stochastic approximation algorithm which employs a weighted combination of the current noisy negative gradient and
some former noisy negative gradient as iterative direction. Both the almost sure convergence and the asymptotic rate of convergence
of the new algorithm are established. Numerical experiments show that the new algorithm outperforms the classical RM algorithm. |
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Keywords: | |
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