报告题目：On dual averaging algorithm for constrained distributed stochastic optimization
报告地点：腾讯会议 ID：870 314 944
In this talk, we consider the constrained stochastic optimization problem over a time-varying random network, where the agents are to collectively minimize a sum of objective functions subject to a common constraint set, we investigate asymptotic properties of a distributed algorithm based on dual averaging of gradients. Different from most existing works on distributed dual averaging algorithms that mainly concentrating on their non-asymptotic properties, we prove not only almost sure convergence and the rate of almost sure convergence, but also asymptotic normality and asymptotic efficiency of the algorithm. To the best of our knowledge, it seems to be the first asymptotic normality result for constrained distributed optimization algorithms.