An Evolving Gradient Resampling Method for Stochastic Optimization
We propose an algorithm for minimizing expected risk F that shares some properties with randomized incremental aggregated gradient methods as well as dynamic sampling methods. Unlike aggregated gradient methods, which are designed to revisit the training set many times, the algorithm proposed here is well suited for problems involving a …