凯发娱乐学术报告
On Chebyshev-Davidson Method for Symmetric Generalized Eigenvalue Problems
苗存强
（中南大学数学与统计凯发K8）
报告时间🟫☝️：2022年8月17日，周三，上午9:00-10:00
报告地点🧝🏼♂️🟫：#腾讯会议：136-216-395 会议密码：0817
报告摘要：As we know, polynomial filtering technique is efficient for accelerating convergence of standard eigenvalue problems, which, however, has not appeared for solving generalized eigenvalue problems. In this talk, by integrating the effectiveness and robustness of the Chebyshev polynomial filters, we propose the Chebyshev-Davidson method for computing some extreme eigenvalues and corresponding eigenvectors of generalized matrix pencils. In this method, both matrix factorizations and solving systems of linear equations are all avoided. Convergence analysis indicates that the Chebyshev-Davidson method achieves quadratic convergence locally. Furthermore, numerical experiments are carried out to demonstrate the convergence properties and to show great superiority and robustness over some state-of-the art iteration methods.
报告人简介：苗存强💆🏽🧚♂️，中南大学数学与统计凯发K8副教授，主要从事科学计算中出现的大规模代数特征值迭代算法的构造及预处理加速👨🏿🦳，在大规模特征值及线性方程组迭代算法的构造及理论分析方面有较好的研究基础⏭，其主要研究成果发表在Journal of Scientific Computing, Numerical Algorithms, Journal of Computational and Applied Mathematics和Linear Algebra and its Applications等期刊。
邀请人🦸🏼♀️： 谢家新
