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- W2285154971 abstract "This work deals with a regularization method enforcing solution sparsity of linear ill-posed problems by appropriate discretization in the image space. Namely, we formulate the so called least error method in an $ell^1$ setting and perform the convergence analysis by choosing the discretization level according to an a priori rule, as well as two a posteriori rules, via the discrepancy principle and the monotone error rule, respectively. Depending on the setting, linear or sublinear convergence rates in the $ell^1$-norm are obtained under a source condition yielding sparsity of the solution. A part of the study is devoted to analyzing the structure of the approximate solutions and of the involved source elements." @default.
- W2285154971 created "2016-06-24" @default.
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- W2285154971 date "2016-07-15" @default.
- W2285154971 modified "2023-10-17" @default.
- W2285154971 title "The least error method for sparse solution reconstruction" @default.
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- W2285154971 doi "https://doi.org/10.1088/0266-5611/32/9/094001" @default.
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