Stopping Rules for Optimization Algorithms Based on Stochastic Approximation
Stopping Rules for Optimization Algorithms Based on Stochastic Approximation
复制标题
基于随机逼近的优化算法的停止规则
DOI：
10.1007/s10957-015-0808-7
复制
发表时间：
2016
影响因子：
1.9
通讯作者：
Yasumasa Fujisaki
中科院分区：
文献类型：
--
作者：
Takayuki Wada;Yasumasa Fujisaki
停止规则的随机优化算法，最小化一个未知的目标函数使用噪声损坏的测量。特别地，考虑了有限差分随机逼近和同时扰动随机逼近。经过足够次数的迭代后的候选解被证明是足够接近最优解的均方意义。这些数字仅由先验信息确定。此外，它表明，这些问题的大小是多项式的顺序。
Stopping rules are developed for stochastic optimization algorithms, which minimize an unknown objective function using noise corrupted measurements. In particular, the finite-difference stochastic approximation and the simultaneous perturbation stochastic approximation are considered. The candidate solution after an adequate number of iterations is shown to be sufficiently close to the optimal solution in a mean squared sense. These numbers are determined by a priori information only. Furthermore, it is shown that these are polynomial order of the problem size.