报告主题：大规模成像问题的SVD近似
报告人：James G. Nagy 教授（Emory 大学）
报告时间：2016年10月27日（周四）14：30
报告地点：校本部G507
邀请人：张建军
主办部门：永利数学系
报告摘要：A fundamental tool for analyzing and solving ill-posedinverse problems is the singular value decomposition (SVD). However, in imaging applications the matrices are often too large to be able to efficiently compute the SVD. In this talk we present a general approach to describe how an approximate SVD can be used to efficiently compute approximate solutions for large-scale ill-posed problems, which can then be used either as an initial guess in a nonlinear iterative scheme, or as a preconditioner for linear iterative methods. We show more specifically how to efficiently compute an SVD approximation for certain applications in image processing.
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