论文标题
一种新型贪婪的高斯 - 赛德尔方法，用于解决大型线性最小二乘问题
A novel greedy Gauss-Seidel method for solving large linear least squares problem
论文作者
论文摘要
我们提出了一种新型的贪婪高斯 - 西德尔方法，用于解决大型线性最小二乘问题。此方法改善了Bai和Wu [Bai ZZ和Wu Wt最近提出的贪婪随机坐标下降（GRCD）方法。关于贪婪的随机坐标下降方法，用于解决大型线性最小二乘问题。数字线性代数应用。 2019; 26（4）：1--15]，进而改善了流行的随机高斯 - 塞德尔方法。提供了新方法的收敛分析。数值实验表明，以相同的精度，我们的方法在计算时间方面优于GRCD方法。
We present a novel greedy Gauss-Seidel method for solving large linear least squares problem. This method improves the greedy randomized coordinate descent (GRCD) method proposed recently by Bai and Wu [Bai ZZ, and Wu WT. On greedy randomized coordinate descent methods for solving large linear least-squares problems. Numer Linear Algebra Appl. 2019;26(4):1--15], which in turn improves the popular randomized Gauss-Seidel method. Convergence analysis of the new method is provided. Numerical experiments show that, for the same accuracy, our method outperforms the GRCD method in term of the computing time.
