关于举行叶筱倞教授学术报告会的通知
时间:  
2019-05-14 14:58:52
  来源:  
自动化科学与工程学院
  作者:  

Title: Decentralized consensus optimization on networks with delayed and stochastic gradients

时间: 2019516日星期四 1500

地点:华南理工大学(五山校区)自动化科学与工程学院3号楼6楼会议室 


Abstract: Decentralized consensus optimization has extensive applications in many emerging big data, machine learning, and sensor network problems. In decentralized computing, nodes in a network privately hold parts of the objective function and need to collaboratively solve for the consensual optimal solution of the total objective, while they can only communicate with their immediate neighbors during updates. In real-world networks, it is often difficult and sometimes impossible to synchronize these nodes, and as a result they have to use stale and stochastic gradient information which may steer their iterates away from the optimal solution. In this talk, we focus on a decentralized consensus algorithm by taking the delays of gradients into consideration. We show that, as long as the random delays are bounded in expectation and a proper diminishing step size policy is employed, the iterates generated by this algorithm still converge to a consensual optimal solution. Convergence rates of both objective and consensus are derived. Numerical results on some synthetic optimization problems and on real seismic tomography will also be presented.


Biography

Dr. Xiaojing Ye is currently a tenure-track assistant professor at the Department of Mathematics and Statistics in Georgia State University, Atlanta, USA. Prior to joining Georgia State University in 2013, Dr. Ye was a visiting assistant professor at the School of Mathematics in Georgia Institute of Technology, USA. Dr. Ye received his doctoral degree in mathematics from the University of Florida, USA in 2011, the master’s degree in statistics from the University of Florida in 2009, and the bachelor’s degree in mathematics from Peking University in 2006. Dr. Ye’s research focuses on applied and computational mathematics, particularly PDE-based image analysis, numerical optimization, analysis and computations of stochastic differential equations, machine learning.