Faster Stochastic Algorithms via History-Gradient Aided Batch Size Adaptation
share
Various schemes for adapting batch size have been recently proposed to accelerate stochastic algorithms. However, existing schemes either apply prescribed batch size adaption or require additional backtracking and condition verification steps to exploit the information along optimization path. In this paper, we propose an easy-to-implement scheme for adapting batch size by exploiting history stochastic gradients, based on which we propose the Adaptive batch size SGD (AbaSGD), AbaSVRG, and AbaSPIDER algorithms. To handle the dependence of the batch size on history stochastic gradients, we develop a new convergence analysis technique, and show that these algorithms achieve improved overall complexity over their vanilla counterparts. Moreover, their convergence rates are adaptive to the optimization landscape that the iterate experiences. Extensive experiments demonstrate that our algorithms substantially outperform existing competitive algorithms.
READ FULL TEXT
