论文信息 - Optimal Convergence Rates Results for Linear Inverse Problems in Hilbert Spaces

Optimal Convergence Rates Results for Linear Inverse Problems in Hilbert Spaces

ABSTRACT In this article, we prove optimal convergence rates results for regularization methods for solving linear ill-posed operator equations in Hilbert spaces. The results generalizes existing convergence rates results on optimality to general source conditions, such as logarithmic source conditions. Moreover, we also provide optimality results under variational source conditions and show the connection to approximative source conditions.

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