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• W2570594503 endingPage "025004" @default.
• W2570594503 startingPage "025004" @default.
• W2570594503 abstract "We propose a preconditioned alternating direction method of multipliers (ADMM) to solve linear inverse problems in Hilbert spaces with constraints, where the feature of the sought solution under a linear transformation is captured by a possibly non-smooth convex function. During each iteration step, our method avoids solving large linear systems by choosing a suitable preconditioning operator. In case the data is given exactly, we prove the convergence of our preconditioned ADMM without assuming the existence of a Lagrange multiplier. In case the data is corrupted by noise, we propose a stopping rule using information on noise level and show that our preconditioned ADMM is a regularization method; we also propose a heuristic rule when the information on noise level is unavailable or unreliable and give its detailed analysis. Numerical examples are presented to test the performance of the proposed method." @default.
• W2570594503 created "2017-01-13" @default.
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• W2570594503 date "2017-01-09" @default.
• W2570594503 modified "2023-09-27" @default.
• W2570594503 title "Preconditioned alternating direction method of multipliers for inverse problems with constraints" @default.
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• W2570594503 doi "https://doi.org/10.1088/1361-6420/33/2/025004" @default.
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