一、报告题目：A Transformed Tubal Tensor Train Decomposition Method for Internet Traffic Data Recovery and Forecast

二、报告人：凌晨 教授

三、报告时间：2026年6月17日 14:00-15:00

四、报告地点：闻理园A3-217

报告摘要: 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 rafficdata, 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 trafficdata and the trafficdata 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.

报告人简介：杭州电子科技大学理学院（二级）教授，博士生导师。曾任：杭州电子科技大学亚洲博彩app 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。现任：ESI期刊 Pacific Journal of Optimization编委、国际期刊Statistics, Optimization & Information Computing编委。研究方向：非线性规划、变分不等式与互补问题、张量计算、多变量多项式优化、半无限规划、随机规划、多目标优化理论与应用等。近十五年来，连续主持国家自科基金和浙江省自科基金各多项，其中省基金重点项目1项。在国内外重要刊物发表论文100余篇，其中SCI期刊论文90余篇，多篇发表在Math. Program.、SIAM J. on Optim.和 SIAM J.on Matrix Anal.and Appl. 、COAP、JOTA、JOGO等。

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