A randomized method for handling a difficult function in a convex optimization problem, motivated by probabilistic programming
Csaba I. Fábián, Edit Csizmás, Rajmund Drenyovszki, Tibor Vajnai, Lóránt Kovács, Tamás Szántai · Annals of Operations Research · 2019
Abstract We propose a randomized gradient method for handling a convex function whose gradient computation is demanding. The method bears a resemblance to the stochastic approximation family. But in contrast to stochastic approximation, the present method builds a model problem. The approach is adapted to probability maximization and probabilistic constrained problems. We discuss simulation procedures for gradient estimation.