The authors examine a novel class of stochastic approximation procedures which are based on carefully controlling the number of observations or measurements taken before each computational iteration. This method, referred to as sampling controlled stochastic approximation, has advantages over standard stochastic approximation such as requiring less computation and the ability to handle bias in estimation. The authors address the growth rate required of the number of samples and prove a general convergence theorem for the proposed stochastic approximation method. In addition, they present applications to optimize and also derive a sampling controlled version of the classic Robbins-Munro algorithm. >
