A stopping rule for stochastic approximation
A stopping rule for stochastic approximation
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DOI：
10.1016/j.automatica.2015.06.029
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发表时间：
2015-10
期刊：
影响因子：
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通讯作者：
T. Wada;Y. Fujisaki
中科院分区：
文献类型：
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作者：
T. Wada;Y. Fujisaki
随机逼近算法是通过噪声测量来寻找未知非线性方程的解的递归过程。在这篇文章中，我们给出了一个随机逼近的停止规则。我们证明了当随机近似按照我们的停止规则停止时，精确解和候选解之间的距离很有可能小于指定的容差水平。此外，停止规则所需的递归次数是问题大小的多项式函数。
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.