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发表时间:2020-11-25 阅读次数:227次
报告题目: Joint Fudan - RICAM Seminar on Inverse Problems
报 告 人:Stefan Kindermann
报告人所在单位:Johannes Kepler University Linz
报告日期:2020-11-25 星期三
报告时间:20:00-21:00
报告地点:https://www.gotomeet.me/Ricam2/joint-fudan---ricam-seminar
  
报告摘要:
This talk is about the heuristic (or data-driven) choice of the regularization parameter in the regularization theory of ill-posed problems. Here, heuristic means that the parameter is chosen independent of the knowledge of the noise level (or any other supplementary information).
Recently, a convergence theory for several heuristic parameter choice methods (for linear regularization) has been developed on basis of the so-called noise-restricted convergence analysis. Withing this framework, one can circumvent the restrictions of the so-called Bakushinskii veto.
We outline the corresponding theory and present theoretical results for the most important examples of heuristic parameter choice rules.
We furthermore discuss some recent results in this direction for convex, nonlinear Tikhonov regularization, together with some open research questions.
  
本年度学院报告总序号:287

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