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Two-subspace randomized extended Kaczmarz method for linear least-squares problems

巫文婷

（北京理工大学数学与统计学院）

报告时间：2022年8月17日，星期三，上午10:00-11:00

报告地点：#腾讯会议：136-216-395 会议密码：0817

报告摘要：Based on a generalization of the two-subspace randomized Kaczmarz method without the assumptions of unit row norms and full column rank on the coefficient matrix, we propose a block version of the randomized extended Kaczmarz method for solving the large-scale linear least-squares problem, called the two-subspace randomized extended Kaczmarz method. This block method does not require any row or column paving. Theoretical analysis and numerical results show that this method is much more efficient than the randomized extended Kaczmarz method, especially when the correlation between the rows or the columns of the coefficient matrix is close. When the coefficient matrix is of full column rank, it can also outperform the randomized coordinate descent method in some cases.

报告人简介：巫文婷，中国科学院数学与系统科学研究院博士，北京理工大学数学与统计学院特别副研究员。研究方向为数值代数、随机迭代方法。2019年获第七届中国数学会计算数学分会“应用数值代数奖”。担任Numerical Linear Algebra with Applications期刊编委。论文发表在SIAM Journal on Scientific Computing等期刊上。

邀请人： 谢家新