报告题目 (Title): Refined Error Bounds for Relaxed Greedy Randomized Kaczmarz Methods（松弛贪婪随机Kaczmarz算法的精细误差界）
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)：2026年10月17日(周六) 18:00
报告地点 (Place)：上海大学宝山校区GJ302
邀请人(Inviter)：张建军
报告摘要: By rigorously analyzing the asymptotic convergence property of the relaxed greedy randomized Kaczmarz method, we obtain an upper bound on its asymptotic convergence rate that is sharper than the existing one. Also, this upper bound naturally leads to a strengthened convergence guarantee for the greedy randomized Kaczmarz method. Consequently this result improves and enriches the convergence theory of the greedy randomized Kaczmarz method with or without a relaxation parameter, when these iteration methods are applied to solve large-scale and consistent system of linear equations.