Confidence Region for Distributed Stochastic Optimization Problem in Stochastic Gradient Tracking Method

Since stochastic approximation (SA) based algorithms are easy to implement and need less memory, they are very popular in distributed stochastic optimization problems. Many works have focused on the consistency of the objective values and the iterates returned by the SA based algorithms. It is of fundamental interest how to quantify the uncertainty associated with … Read more