Stochastic Compositional Gradient Descent: Algorithms for Minimizing Compositions of Expected-Value Functions

Abstract

Classical stochastic gradient methods are well suited for minimizing expected-value ob-jective functions. However, they do not apply to the minimization of a nonlinear function involving expected values or a composition of two expected-value functions, i.e., problems of the form minxEv f