preconditioned gmres methods for the least square problems
报告人简介
殷俊锋，同济大学教授，创新创业学院副院长。主持和参与国家级和省部级以上科研项目10余项，发表论文60余篇，指导多项团队在各类创新大赛中获奖。学术兼职包括中国工业与应用数学学会副秘书长，中国创造学会副秘书长等。参与国家教学成果二等奖1项和上海市教学成果奖2项。
内容简介
after reviewing the numerical solution of large sparse least square problems, we propose kaczmarz-type preconditioned gmres method, as well as sketching techniques. theoretical analyses is given to guarantee the convergence and parameter tuning strategies are studied in details. numerical experiments on least square problems further verify the efficiency of the preconditioners and show that the proposed method is superior to the existing preconditioned gmres method.
