A stochastic gradient algorithm for non-separable optimization with convergence guarantee

Yingzhou Li, Ruofan Wu

We study non-separable objectives in which the loss depend on dataset-level quantities. We introduce an SGD-style framework that employs two batch-gradient constructs: the ideal pe…