应数学与计算科学学院、广西应用数学中心(c7(中国)官网c7(中国)官网首页)及广西高校数据分析与计算重点实验室邀请，杭州电子科技大学喻高航教授和广西大学唐春明教授将于2023年3月11日通过腾讯会议网络平台线上讲学，欢迎全校师生踊跃参加。报告具体安排如下：

时间：2023年3月11日（周六）上午8：30-12：30

地点：腾讯会议（会议号：640873981）

报告题目一：Practical Sketching Algorithms for Low-Rank Tucker Approximation of Large Tensors

主讲人：杭州电子科技大学 喻高航 教授

报告摘要：Low-rank approximation of tensors has been widely used in high-dimensional data analysis. It usually involves singular value decomposition (SVD) of large-scale matrices with high computational complexity. Sketching is an effective data compression and dimensionality reduction technique applied to the low-rank approximation of large matrices. This talk presents two practical randomized algorithms for low-rank Tucker approximation of large tensors based on sketching and power scheme, with a rigorous error-bound analysis. Numerical experiments on synthetic and real-world tensor data demonstrate the competitive performance of the proposed algorithms.

主讲人简介：

喻高航，杭州电子科技大学教授，博士，博士生导师，主要从事张量数据分析、大规模优化计算及其在机器学习、图像处理与医学影像中的应用研究。先后在SIAM Journal on Imaging Sciences, International Journal of Robust and Nonlinear Control，IEEE Signal Processing Letters，Journal of Mathematical Imaging and Vision等国际期刊上发表40余篇SCI论文,先后主持5项国家自然科学基金、1项教育部新世纪优秀人才支持计划项目和1项浙江省自然科学基金重大项目，有多篇论文入选ESI高被引榜单。自2013年起任国际学术期刊Statistics, Optimization and Information Computing执行编委（Coordinating Editor）。

报告题目二：A restricted memory quasi-Newton bundle method for nonsmooth optimization on Riemannian manifolds

主讲人：广西大学 唐春明 教授

报告摘要：In this talk, a restricted memory quasi-Newton bundle method for minimizing a locally Lipschitz function over a Riemannian manifold is proposed, which extends the classical ones in Euclidean space to the manifold setting. The potential second order information of the objective function is approximated by applying the Riemannian versions of the quasi-Newton updating formulas. The subgradient aggregation technique is used to avoid solving the time-consuming quadratic programming subproblem when calculating the candidate descent direction. Moreover, a new Riemannian line search procedure is proposed to generate the stepsizes. Global convergence of the proposed method is established: if the serious iteration steps are finite, then the last serious iteration is stationary; otherwise every accumulation point of the serious iteration sequence is stationary. Finally, some preliminary numerical results show that the proposed method is promising.

主讲人简介：

唐春明，广西大学数学与信息科学学院教授，博士，博士生导师，广西运筹学会副理事长，中国运筹学会理事，广西数学会常务理事。1998-2004年本、硕就读于广西大学，2008年博士毕业于上海大学，2014年到澳大利亚新南威尔士大学访学一年。目前主要研究非光滑优化算法。主持国家自然科学基金项目4项，广西自然科学基金项目3项（含广西杰青1项）。作为主要参与者获广西自然科学奖二等奖2项。在《European Journal of Operational Research》、《Journal of Optimization Theory and Applications》、《Computational Optimization and Applications》、《Optimization Letters》、《Optimization》、《Numerical Algorithms》、《IEEE Transactions on Power Systems》和《中国科学：数学》等重要刊物发表论文40余篇。