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Fast off-the-grid sparse recovery with over-parametrized projected gradient descent - MaRDI portal

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Fast off-the-grid sparse recovery with over-parametrized projected gradient descent

From MaRDI portal
Publication:6392328

arXiv2202.13757MaRDI QIDQ6392328

Author name not available (Why is that?)

Publication date: 28 February 2022

Abstract: We consider the problem of recovering off-the-grid spikes from Fourier measurements. Successful methods such as sliding Frank-Wolfe and continuous orthogonal matching pursuit (OMP) iteratively add spikes to the solution then perform a costly (when the number of spikes is large) descent on all parameters at each iteration. In 2D, it was shown that performing a projected gradient descent (PGD) from a gridded over-parametrized initialization was faster than continuous orthogonal matching pursuit. In this paper, we propose an off-the-grid over-parametrized initialization of the PGD based on OMP that permits to fully avoid grids and gives faster results in 3D.




Has companion code repository: https://github.com/pjbenard/opcomp_sparse_recovery








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