报告题目：Data-Driven Optimal Iterative Parameter Prediction and its Applications

报告摘要：Matrix splitting iterative methods with parameters play a crucial role in solving linear systems. How to choose optimal splitting parameters is a key problem. In this talk, we propose a data-driven approach for predicting optimal iterative parameters: multi-task kernel learning Gaussian regression prediction (GPR) method. We develop the generalized alternating direction implicit (GADI) framework with optimal parameters, successfully integrating it as a smoother in algebraic multigrid methods to solve linear systems. Moreover, we accelerate GPR using mixed precision strategy and evaluate the predicted results with statistical indicators. Further, we have successfully applied GPR to (time-dependent) linear algebraic systems (elliptic equations, Poisson equations, convection-diffusion equations, Helmholtz equations) and linear matrix equations (Sylvester equations). Numerical results illustrate our methods can save an enormous amount of time in selecting the relatively optimal splitting parameters compared with the exists methods. When the system size exceeds hundreds of thousands, the acceleration ratio of the GADI framework can reach hundreds to thousands of times.

报告人简介：张娟，教授，博士生导师，湘潭大学数学与计算科学学院副院长，“智能计算与信息处理”教育部重点实验室常务副主任。入选湖湘青年英才，省青年骨干教师培养对象。多次赴新加坡国立大学、澳门大学访问。主持国自科面上、开元国际研究院课题、711研究所项目。作为子课题负责人承担国家重点研发计划、军科委GF项目、工信部项目、XXX外协项目。主要从事数值代数、控制理论研究。近5年在计算数学、控制领域、数据科学权威期刊SINUM、SISC、Automatica、IEEE TKDE、JCP、JSC、CSIAM-AM 发表学术论文20余篇。

报告时间：2026年5月28日（星期四）16:30-17:30

报告地点：同析4号楼 数学学院308