SPSA的发展与应用
An overview of SPSA: recent development and applications
摘要：
在不确定环境下求解高维最优化问题的需求越来越大。同时扰动随机逼近（SPSA）算法是求解高维最优化问题的一种有效算法，在求解高维最优化问题时不能得到解析解. SPSA的目的是通过在每次迭代时对随机维度子集施加扰动来估计梯度。SPSA可以容易地实现并且是高效的，因为它依赖于目标函数的测量，而不是目标函数的梯度的测量。自SPSA被提出以来，它已被应用于强化学习、生产优化等多个领域。本文简要介绍了SPSA的最新发展及其应用。
英文摘要：
There is an increasing need in solving high-dimensional optimization problems under non-deterministic environment. The simultaneous perturbation stochastic approximation (SPSA) algorithm has recently attracted considerable attention for solving high-dimensional optimization problems where the analytical formula cannot be attained. SPSA is designed to estimate the gradient by applying perturbation on a random subset of dimensions at each iteration. SPSA can be easily implemented and is highly efficient in that that it relies on measurements of the objective function, not on measurements of the gradient of the objective function. Since its invention, SPSA has been implemented in various fields such as reinforcement learning, production optimization etc. The paper briefly discuss the recent development of SPSA and its applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
