Unmatched Preconditioning of the Proximal Gradient Algorithm
نویسندگان
چکیده
This work addresses the resolution of penalized least-squares problems using proximal gradient algorithm (PGA). PGA can be accelerated by preconditioning strategies. However, typical effective choices preconditioners may correspond to intricate matrices that are not easily inverted, leading increased complexity in computation proximity step. To relax these requirements, we propose an unmatched approach where two metrics used step and We provide convergence conditions for this new iterative scheme characterize its limit point. Simulations tomographic image reconstruction from undersampled measurements show benefits our various simple metrics.
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ژورنال
عنوان ژورنال: IEEE Signal Processing Letters
سال: 2022
ISSN: ['1558-2361', '1070-9908']
DOI: https://doi.org/10.1109/lsp.2022.3169088