报告题目：Accelerated greedy randomized augmented Kaczmarz algorithm for inconsistent linear systems

报告专家：刘永博士，常熟理工学院

报告地点：腾讯会议(会议号328-171-9712;密码314159)

报告时间：2024年1月4日晚7:30-9:30

报告摘要：For solving inconsistent linear systems of equations iteratively, we further generalize the greedy randomized augmented Kaczmarz (GRAK) algorithm by introducing a nonzero parameter in the involved augmented linear system of the above inconsistent linear system, obtaining a class of accelerated greedy randomized augmented Kaczmarz algorithms.These algorithms involve one iteration parameter whose special choice can recover the GRAK algorithm, as well as yield new ones. Theoretical analyses show that the new algorithms converge to the unique solution of the augmented linear system. Moreover, the optimal choice of the parameter involved and the corresponding convergence rates of the new algorithms are computed exactly. Numerical results show that our algorithms can be much more effective than the GRAK algorithm in terms of both iteration counts and computing times.

报告人简介：刘永博士，常熟理工学院数学与统计学院讲师，2020年毕业于上海大学计算数学专业，主要研究方向为随机数值代数，现主持江苏省高等学校面上项目一项，发表论文10余篇

作者：吴念慈；编辑：刘鹍；审核：郭晖；发布：郭敏