Abstract

Due to rapid growth of data sizes in practical applications, in recent years stochastic optimization methods have received tremendous attention and proved to be efficient in various applications of science and technology including in particular the machine learning applications. In this talk we propose some stochastic iteration methods for solving linear inverse problems. The convergence and the convergence rate are provided. Several numerical examples validate the efficiency of the proposed algorithms.