A Stopping Rule for Linear Stochastic Approximation with Martingale Difference Noise

抄録

The stochastic approximation is an iterative algorithm used to solve an unknown equation via noise corrupted observation of its residual. This paper develops its stopping rule under a martingale difference noise. This rule is a sufficient number of iterations to theoretically guarantee the distance between a current solution candidate and its solution. Its effectiveness is demonstrated through a numerical example.