报告题目：On Convergence Rates of Kaczmarz-Type Methods with Different Selection Rules of Working Rows

报告人：白中治 研究员

报告时间：2024年7月15日（星期一）9:30

报告地点：文理楼254

报告摘要：

The Kaczmarz method is a classical while effective iteration method for solving very large-scale consistent systems of linear equations, and the randomized Kaczmarz method is an important and valuable variant of the Kaczmarz method. 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. Here the mean-squared distance is defined as the mean-value of the squared Euclidean norm of the current update increment of the iteration, and the mean-squared error is defined as the mean-value of the squared Euclidean norm of the current error that is the difference between the current iterate and the true solution of the target linear system.

报告人简介：

白中治，中国科学院数学与系统科学研究院研究员、博士生导师,俄罗斯南部联邦大学荣誉博士。曾获得国家杰出青年科学基金、冯康科学计算奖和国务院政府特殊津贴等，并入选国家级“新世纪百千万人才工程计划”和中国科学院百人计划(D类)。他曾多次应邀在重要国际会议上做主旨邀请报告；多次担任重要国际会议的共同主席，及组织委员会或科学委员会成员；也曾担任至少十五种国际国内学术刊物的编委。白中治研究员的主要研究领域为数值代数、数值优化、并行计算和微分方程数值解等；他为线性与非线性代数方程组、代数Riccati方程、代数特征值问题、离散互补问题、离散整数及分数阶微分方程的数值求解设计了高效的串行和并行迭代方法，并建立了系统深刻的收敛性理论。白中治研究员连续多次在爱思唯尔中国高被引学者榜单中名列前茅，并于2016、2017、2018、2019和2020年连续五次跻身于汤森路透 ISI Web of Science 全球高被引科学家行列。特别，他在2003年与美国科学院、工程院和艺术科学院院士、斯坦福大学教授Gene H. Golub等所提出的HSS迭代方法被公认为是矩阵计算的里程碑，也是线性代数方程组迭代方法研究领域近二十年来最重要的进展之一。