报告题目：How likely can a point be in different Cantor sets

报 告 人：李文侠

时 间：2021年4月17日（周六）下午15:00-16:00

地 点：六号楼2楼报告厅

简 介：

报告题目：Graph Sparsification by Universal Greedy Algorithms

报 告 人：谢家新

时 间：2021年4月23号（周五）下午3:00-4:00

地 点：腾讯会议（会议号请联系张雄军老师索取）

简 介：Graph sparsification is to approximate an arbitrary graph by a sparse graph and is useful in many applications, such as simplification of social networks, least squares problems, numerical solution of symmetric positive definite linear systems and etc. In this talk, inspired by the well-known sparse signal recovery algorithm called orthogonal matching pursuit (OMP), we introduce a deterministic, greedy edge selection algorithm called universal greedy approach (UGA) for graph sparsification. The convergent results as well as the computational complexity are discussed. Extensive experimental results, including applications to graph clustering and least squares regression, show the effectiveness of proposed approaches.

报告题目：A Momentum Block-Randmoized Stochastic Algorithm for L…

报 告 人：崔春风

时 间：2021年4月23号（周五）下午2:00-3:00

地 点：腾讯会议（会议号请联系张雄军老师索取）

简 介：The block-randomized stochastic algorithm has shown its power in handling high-dimensional low-tank tensor canonical polyadic decomposition (CPD). Since computing CPD is computationally expensive, there is great interest in speeding up the convergence. In this talk, we introduce a momentum accelerated version of the block-randomized stochastic gradient descent (SGD) algorithm for low-rank tensor CPD. Under some mild conditions, we show the global convergence to the stationary point of the fixed stepsize algorithm for this nonconvex nonsmooth optimization problem. Compared with the algorithms without momentum, the preliminary numerical experiments for the synthetic and real data demonstrated that our accelerated algorithms are efficient, and can achieve better performance in terms of objection function value, mean squared error, and structural similarity value. This talk is based on the joint work with Qingsong Wang and Deren Han.