报告人：何洪津教授

报告题目：Tensor Completion: Optimization Models and Algorithms

摘要：Tensor completion refers to the task of estimating the missing data from an incomplete measurement or observation, which is a core problem frequently arising from the areas of big data analysis, computer vision, and network engineering. Due to the multi-dimensional nature of high-order tensors, the matrix approaches, e.g., matrix factorization and direct matricization of tensors, are often not ideal for tensor completion and recovery. In this talk, we will introduce some optimization models and algorithms, especially exploiting the possibly inherent sparsity and low-rankness of tensors for tensor completion. A series of computational experiments on real-world data sets, including internet traffic data sets, color images, and face recognition, demonstrate that our model performs better than many existing state-of-the-art matricization and tensorization approaches in terms of achieving higher recovery accuracy.（Joint work with Chen Ling, Chenjian Pan, Liqun Qi, Wenhui Xie, and Yanwei Xu）

报告时间：2022年10月12日上午09:30-12:00

报告形式：腾讯会议，会议号：206 341 794

获取会议密码请联系：wufanmath@163.com

报告人简介：何洪津，宁波大学数学与统计学院教授。2012年6月博士毕业于南京师范大学计算数学专业，导师孙文瑜教授和韩德仁教授。主要研究兴趣为最优化理论、算法和相关应用，相关成果发表在Numerische Mathematik、Inverse Problems、Journal of Scientific Computing等计算数学权威期刊。主持和参与国家、省自然科学基金多项。