报告题目：Tensor: Optimization and Computations with Applications in Imaging Sciences

报告摘要： In this presentation, we would like to investigate some topics on "Tensor: Optimization and Computations with Applications in Imaging Sciences". Firstly, an adaptive gradient (AG) method is presented for generalized tensor eigenpairs. Global convergence and linear convergence rate could be established under some suitable conditions. Numerical results are reported to illustrate the efficiency of the proposed AG method. Comparing with the GEAP method, an adaptive shifted power method proposed by Tamara G. Kolda and Jackson R. Mayo [SIAM J. Matrix Anal. Appl., 35 (2014), pp. 1563-1581], the AG method is much faster and could reach the largest eigenpair with a higher probability. Secondly, we would like to show some high order tensor models with application to characterize non-Gaussian diffusion processes in diffusion tensor imaging (DTI).

报告人：喻高航，教授

时 间：2017年3月21日周二下午3:20

地 点：18-918（理学院会议室）

人物名片：

喻高航博士、教授，教育部新世纪优秀人才支持计划人选，美国数学协会数学评论特约评论员，江西省数学学会理事，江西省百千万工程人选。主要从事张量数据分析，优化计算及其在医学影像、图像处理中的相关应用研究, 最近几年在张量特征计算及其应用方面取得比较好的学术成果,分别发表在国际顶级刊物SIAM Journal on Imaging Sciences, Journal of Mathematical Imaging and Vision上，其中与澳大利亚新南威尔士大学李国胤博士和香港理工大学祁力群教授合作2013年发表在Numerical Linear Algebra with Applications上的一篇文章被ISI收录为高被引文章(ESI Top Papers / Highly Cited Papers)。主持多项国家自然科学基金项目和省部级课题，2013年起任国际学术期刊Statistics, Optimization and Information Computing 执行编委（Coordinating Editor）。