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Implementation of the simultaneous perturbation algorithm forstochastic optimization

机译：随机优化同时摄动算法的实现

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摘要

The need for solving multivariate optimization problems is pervasive in engineering and the physical and social sciences. The simultaneous perturbation stochastic approximation (SPSA) algorithm has recently attracted considerable attention for challenging optimization problems where it is difficult or impossible to directly obtain a gradient of the objective function with respect to the parameters being optimized. SPSA is based on an easily implemented and highly efficient gradient approximation that relies on measurements of the objective function, not on measurements of the gradient of the objective function. The gradient approximation is based on only two function measurements (regardless of the dimension of the gradient vector). This contrasts with standard finite-difference approaches, which require a number of function measurements proportional to the dimension of the gradient vector. This paper presents a simple step-by-step guide to implementation of SPSA in generic optimization problems and offers some practical suggestions for choosing certain algorithm coefficients
机译：解决多元优化问题的需求在工程以及物理和社会科学领域普遍存在。同步摄动随机逼近（SPSA）算法近来引起了相当大的注意力，用于挑战性优化问题，在这些问题中，很难或不可能直接获得目标函数相对于要优化参数的梯度。 SPSA基于易于实现且高效的梯度近似，该梯度近似依赖于目标函数的度量，而不是目标函数的梯度的度量。梯度近似仅基于两个函数测量值（与梯度向量的维数无关）。这与标准的有限差分方法形成对比，后者需要进行大量与梯度矢量的大小成比例的函数测量。本文提出了在一般优化问题中实施SPSA的简单分步指南，并为选择某些算法系数提供了一些实用建议

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