论文标题
同时扰动随机近似：迈向一次迭代的测量
Simultaneous perturbation stochastic approximation: towards one-measurement per iteration
论文作者
论文摘要
当测量要最小化的函数的值不仅昂贵，而且使用噪声，同时流动的扰动随机近似（SPSA）算法仅需要两个函数值。在本文中，我们提出了一种在平均意义上仅需要一个函数测量值的方法。我们证明了新算法的强烈收敛性和渐近正态性。实验结果显示了我们算法的有效性和潜力。
When measuring the value of a function to be minimized is not only expensive but also with noise, the popular simultaneous perturbation stochastic approximation (SPSA) algorithm requires only two function values in each iteration. In this paper, we propose a method requiring only one function measurement value per iteration in the average sense. We prove the strong convergence and asymptotic normality of the new algorithm. Experimental results show the effectiveness and potential of our algorithm.
