Note on Learning Rate Schedules for Stochastic Optimization

Abstract

We present and compare learning rate schedules for stochastic gradient descent, a general algorithm which includes LMS, on-line backpropagation and k-means clustering as special cases. We introduce search-then-converge type schedules which outperform the classical constant and running average (1/t) schedules both in speed of convergence and quality of solution.