报告题目 (Title)：不同行选择Kaczmarz型算法的收敛性
报告人 (Speaker)：白中治 研究员（中国科学院数学与系统科学研究院）
报告时间 (Time)：2025年11 月17日（周一）18:30
报告地点 (Place)：地腾讯会议：390-527-525
邀请人(Inviter)：杨永建、谭福平
报告摘要： By theoretically analyzing and numerically experimenting several criteria typically adopted in the non-randomized and the randomized Kaczmarz method for selecting the working row, we derive sharper upper bounds for the convergence rates of some of the correspondingly induced Kaczmarz-type methods including those with respect to the maximal residual, maximal distance, and distance selection rules of the working row, and, for this whole suite of iteration methods consisting of the Kaczmarz methods with respect to the uniform, non-uniform, residual, distance, maximal residual, and maximal distance selection rules of the working row, we reveal their comparable relationships in terms of both mean-squared distance and mean-squared error, and show their computational effectiveness and numerical robustness based upon implementing a large number of test examples.