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Generalized Qualification and Qualification Levels for Spectral Regularization Methods

机译:光谱正则化方法的广义资格和资格级别

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The concept of qualification for spectral regularization methods (SRM) for inverse ill-posed problems is strongly associated to the optimal order of convergence of the regularization error (Engl et al. in Regularization of inverse problems. Mathematics and its applications, vol. 375, Kluwer Academic, Dordrecht, 1996; Math, in SIAM J. Numer. Anal. 42(3):968-973, 2004; Math, and Pereverzev in Inverse Probl. 19(3):789-803, 2003; Vainikko in USSR Comput. Math. Math. Phys. 22(3): 1-19, 1982). In this article, the definition of qualification is extended and three different levels are introduced: weak, strong and optimal. It is shown that the weak qualification extends the definition introduced by Math, and Pereverzev (Inverse Probl. 19(3):789-803, 2003), mainly in the sense that the functions associated with orders of convergence and source sets need not be the same. It is shown that certain methods possessing infinite classical qualification (e.g. truncated singular value decomposition (TSVD), Landweber's method and Showalter's method) also have generalized qualification leading to an optimal order of convergence of the regularization error. Sufficient conditions for a SRM to have weak qualification are provided and necessary and sufficient conditions for a given order of convergence to be strong or optimal qualification are found. Examples of all three qualification levels are provided and the relationships between them as well as with the classical concept of qualification and the qualification introduced in Math, and Pereverzev (Inverse Probl. 19(3):789-803, 2003) are shown. In particular, SRMs having extended qualification in each one of the three levels and having zero or infinite classical qualification are presented. Finally, several implications of this theory in the context of orders of convergence, converse results and maximal source sets for inverse ill-posed problems, are shown.
机译:逆不适定问题的频谱正则化方法(SRM)资格的概念与正则化误差收敛的最佳顺序紧密相关(Engl等人,《逆问题的正则化》,数学及其应用,第375卷, Kluwer Academic,Dordrecht,1996; Math,in SIAM J.Numer.Anal.42(3):968-973,2004; Math and Pereverzev in Inverse Probl。19(3):789-803,2003;苏联Vainikko计算物理数学物理22(3):1-19,1982)。在本文中,对资格的定义进行了扩展,并引入了三个不同的级别:弱,强和最优。结果表明,弱限定条件扩展了Math和Pereverzev(Inverse Probl。19(3):789-803,2003)引入的定义,主要是因为与收敛阶数和源集相关的函数不需要相同。结果表明,某些具有无限经典限定条件的方法(例如,截断奇异值分解(TSVD),Landweber方法和Showalter方法)也具有广义限定条件,从而导致正则化误差的最优收敛阶。提供了SRM具有较弱资格的充分条件,并且找到了给定收敛顺序为强或最佳资格的必要条件和充分条件。提供了所有三个资格级别的示例,并显示了它们之间的关系以及经典资格概念和Math和Pereverzev(Inverse Probl。19(3):789-803,2003)中引入的资格的关系。特别地,提出了在三个级别中的每个级别具有扩展资格并且具有零或无限经典资格的SRM。最后,在收敛阶数,逆结果和逆不适定问题的最大源集的背景下,显示了该理论的几个含义。

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