报告题目：A Transformed Tubal Tensor Train Decomposition Method for Internet Traffic Data Recovery and Forecast
时间：2026年6月8日15：30
地点：阅江楼350
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摘要： Recovery and forecast of network trafficdata from incomplete observed data is an important issue in internet engineering and management. In this talk, 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 (TTT) 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.
个人简介：凌晨,杭州电子科技大学理学院教授（二级），博士生导师。曾任：杭州电子科技大学理学院院长，中国运筹学会数学规划分会副理事长、中国经济数学与管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。现任国际期刊 Pacific Journal of Optimization编委、Statistics, Optimization & Information Computing编委。近十余年来，主持国家自科基金和浙江省自科基金多项(其中省基金重点项目1项)。在Math. Program.、SIAM J. Optim.、SIAM J. Matrix Anal. Appl.、COAP、JOTA、JOGO等国内外重要刊物发表论文多篇。
管理工程学院
2026.6.2
