数学学科Seminar第2910讲 Householder正交化的一些新进展

创建时间:  2025/10/10  邵奋芬   浏览次数:   返回

报告题目 (Title):Some new developments on Householder orthogonalization (Householder正交化的一些新进展)

报告人 (Speaker):邵美悦 教授(复旦大学)

报告时间 (Time):2025年10月14日(周二) 16:00

报告地点 (Place):校本部GJ303

邀请人(Inviter):刘巧华

主办部门:理学院数学系

报告摘要:

Householder orthogonalization plays an important role in numerical linear algebra. It attains perfect orthogonality regardless of the conditioning of the input vectors. However, there are a few issues that have limited the use of Householder orthogonalization. For example, the classical Householder orthogonalization algorithm is only applicable in the standard inner product, and is difficult to apply in the context of a nonstandard inner product. Another case that is frequently encountered in eigenvalue problems is the orthogonalization of a set of vectors against an existing orthogonal basis. Most algorithms for this problem in the literature are based on block Gram–Schmidt orthogonalization, and Householder orthogonalization is rarely studied. We propose solutions to these problems so that the use of Householder orthogonalization is greatly expanded. Theoretical analysis and numerical experiments demonstrate that our approaches are numerically stable under mild assumptions.

上一条:量子科技研究院seminar第73讲暨物理学科Seminar第760讲 零磁通超导电动悬浮研究进展

下一条:数学学科Seminar第2909讲 机器学习中随机牛顿迭代法的切比雪夫加速算法


数学学科Seminar第2910讲 Householder正交化的一些新进展

创建时间:  2025/10/10  邵奋芬   浏览次数:   返回

报告题目 (Title):Some new developments on Householder orthogonalization (Householder正交化的一些新进展)

报告人 (Speaker):邵美悦 教授(复旦大学)

报告时间 (Time):2025年10月14日(周二) 16:00

报告地点 (Place):校本部GJ303

邀请人(Inviter):刘巧华

主办部门:理学院数学系

报告摘要:

Householder orthogonalization plays an important role in numerical linear algebra. It attains perfect orthogonality regardless of the conditioning of the input vectors. However, there are a few issues that have limited the use of Householder orthogonalization. For example, the classical Householder orthogonalization algorithm is only applicable in the standard inner product, and is difficult to apply in the context of a nonstandard inner product. Another case that is frequently encountered in eigenvalue problems is the orthogonalization of a set of vectors against an existing orthogonal basis. Most algorithms for this problem in the literature are based on block Gram–Schmidt orthogonalization, and Householder orthogonalization is rarely studied. We propose solutions to these problems so that the use of Householder orthogonalization is greatly expanded. Theoretical analysis and numerical experiments demonstrate that our approaches are numerically stable under mild assumptions.

上一条:量子科技研究院seminar第73讲暨物理学科Seminar第760讲 零磁通超导电动悬浮研究进展

下一条:数学学科Seminar第2909讲 机器学习中随机牛顿迭代法的切比雪夫加速算法