Abstract

In this talk, I will discuss the stochastic gradient descent for solving linear and nonlinear inverse problems. Such algorithms have been very popular in a number of practical inverse problems. However, the relevant mathematical theory in the context of ill-posed inverse problems remains largely missing. In this talk, I will present some recent results in the direction, and illustrate the theory with numerical examples.