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arXiv 1409.7610math.FAcs.NAmath.NA
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在Hilbert空间中线性反问题的广义收敛速率结果

Generalized Convergence Rates Results for Linear Inverse Problems in Hilbert Spaces

Johann Radon Institute for Computational and Applied Mathematics(约翰·拉东计算与应用数学研究所);University of Vienna(维也纳大学);Purdue University(普渡大学);University of Minnesota(明尼苏达大学);RICAM

Roman Andreev, Peter Elbau, Maarten V. de Hoop, Lingyun Qiu, Otmar Scherzer

AI总结:

本文研究了在Banach空间中推广的Tikhonov正则化方法的收敛速率条件,指出变分源条件与Hilbert空间中的标准源条件不等价,但通过Neubauer的深入结果证明了这些新条件在Hilbert空间中是最佳的,并利用新型源条件扩展了现有收敛速率结果。

摘要:

近年来,一系列关于正则化方法的收敛速率条件已被开发。主要动机是将Hilbert空间中的收敛速率结果推广到Banach空间中的广义Tikhonov正则化。例如,变分源条件已被开发,期望其等同于Hilbert空间中线性反问题的标准源条件。我们证明这一期望不成立。然而,在标准Hilbert空间设置中,这些新条件是最优的,我们通过使用Neubauer的一些深入结果来证明,并扩展了现有的收敛速率结果。我们分析中的关键工具是一种新的源条件,将其与文献中的现有源条件相关联。作为积极的副产品,可以在不使用谱理论的情况下证明收敛速率结果,这是证明Hilbert空间中线性反问题收敛速率的标准技术。

英文摘要:

In recent years, a series of convergence rates conditions for regularization methods has been developed. Mainly, the motivations for developing novel conditions came from the desire to carry over convergence rates results from the Hilbert space setting to generalized Tikhonov regularization in Banach spaces. For instance, variational source conditions have been developed and they were expected to be equivalent to standard source conditions for linear inverse problems in a Hilbert space setting. We show that this expectation does not hold. However, in the standard Hilbert space setting these novel conditions are optimal, which we prove by using some deep results from Neubauer, and generalize existing convergence rates results. The key tool in our analysis is a novel source condition, which we put into relation to the existing source conditions from the literature. As a positive by-product, convergence rates results can be proven without spectral theory, which is the standard technique for proving convergence rates for linear inverse problems in Hilbert spaces.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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