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A stopping rule for stochastic approximation
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DOI：10.1016/j.automatica.2015.06.029.png)
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Abstract
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A stochastic approximation algorithm is a recursive procedure to find the solution to an unknown nonlinear equation via noisy measurements. In this paper, we present a stopping rule for a stochastic approximation. We show that there is a high probability that the distance between the exact solution and the candidate solution is less than a specified tolerance level when the stochastic approximation stops according to our stopping rule. Furthermore, the number of recursions required by the stopping rule is a polynomial function of the problem size. (C) 2015 Elsevier Ltd. All rights reserved.
Keywords:
Stochastic approximation
Randomized algorithms
Adaptive control
System identification
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