j9九游会

上海治理论坛第560期

问题：基于新型张量剖析的互联网流量数据恢复和预测要领

演讲人：破晓教授，，，，，杭州电子科技大学

主持人：林贵华教授，，，，，j9九游会治理学院

时间：2025年11月12日（周三），，，，，上午10:30

所在：j9九游会校本部东区1号楼治理学院420聚会室

主理单位：j9九游会治理学院、j9九游会治理学院青年西席联谊会

演讲人简介：

破晓，，，，，着名优化专家，，，，，杭州电子科技大学二级教授。。曾任中国运筹学会数学妄想分会副理事长、中国经济数学与治理数学研究会副理事长、中国运筹学会理事、中国系统工程学会理事、浙江省数学会常务理事等。。现任Pacific Journal of Optimization、Statistics、Optimization & Information Computing等期刊编委。。主持国家级和省部级项目多项。。在Math Program、SIAM J Optim、SIAM J Matrix Anal Appl等顶级期刊揭晓论文多篇。。

演讲内容简介：

Recovery and forecast of network traffic data from incomplete observed data is an important issue in internet engineering and management. In this paper, by fully considering the temporal stability and periodicity features in internet traffic data, a novel optimization model for internet data recovery and forecast is proposed, which is based upon the newly introduced higher order tensor decomposition form called tubal tensor train decomposition. Moreover, by introducing auxiliary variables and penalty techniques, a relaxation of the proposed model is obtained. Then, an easy-to-operate and effective algorithm for solving the relaxation model is proposed. We prove that the sequence generated by the proposed algorithm converges to a stationary point of the established relaxation model. A series of numerical experiments about the recovery of structurally missing traffic data and the traffic data prediction on the widely used real-world datasets demonstrate that our approach have favorable performance than some state-of-the-art tensor/matrix based approaches.

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