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学术报告

06-07-2021

学习与控制系列学术报告--On dual averaging algorithm for constrained distributed stochastic optimization--陈性敏(大连理工大学)

报告人:陈性敏,大连理工大学数学科学学院

报告题目:On dual averaging algorithm for constrained distributed stochastic optimization

报告时间:2021年7月8日14:00-16:00

报告地点:腾讯会议 ID:870 314 944

报告摘要:

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.

个人简历:

陈性敏,大连理工大学数学科学学院,副教授、硕士生导师。主要研究方向为系统辨识与控制、随机优化与分布式优化、非参数统计与统计学习。主持完成国家自然科学基金项目一项,参与国家自然科学基金项目三项。

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