On the extended randomized multiple row method for solving linear least-squares problems
报告摘要：The randomized row method is a popular representative of the iterative algorithm because of its efficiency in solving the overdetermined and consistent systems of linear equations. In this paper, we present an extended randomized multiple row method to solve a given overdetermined and inconsistent linear system and analyze its computational complexities at each iteration. We prove that the proposed method can linearly converge in the mean square to the least-squares solution with a minimum Euclidean norm. Several numerical studies are presented to corroborate our theoretical findings. The real-world applications, such as image reconstruction and large data fitting in computer-aided geometric design, are also presented for illustration purposes.
报告人简介：吴念慈，2020年6月毕业于武汉大学，获计算数学博士学位；同年8月，进入中南民族大学数统学院工作。研究方向为数值代数、反问题等，现已在Inverse Problems、Numerical Linear Algebra with Applications等计算数学专业杂志上发表SCI论文多篇。