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On the uniqueness of solutions for the basis pursuit in the continuum
This paper studies the continuous-domain inverse problem of recovering Radon measures on the one-dimensional torus from low-frequency Fourier coefficients, where K c is the cutoff frequency. Our approach consists in minimizing the total-variation norm among all Radon measures that are consistent wit...
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Published in: | Inverse problems 2022-12, Vol.38 (12), p.125005 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | This paper studies the continuous-domain inverse problem of recovering Radon measures on the one-dimensional torus from low-frequency Fourier coefficients, where
K
c
is the cutoff frequency. Our approach consists in minimizing the total-variation norm among all Radon measures that are consistent with the observations. We call this problem the basis pursuit in the continuum (BPC). We characterize the solution set of (BPC) in terms of uniqueness and describe its sparse solutions which are sums of few signed Dirac masses. The characterization is determined by the spectrum of a Toeplitz and Hermitian-symmetric matrix that solely depends on the observations. More precisely, we prove that (BPC) has a unique solution if and only if this matrix is neither positive definite nor negative definite. If it has both a positive and negative eigenvalue, then the unique solution is the sum of at most 2
K
c
Dirac masses, with at least one positive and one negative weight. If this matrix is positive (respectively negative) semi-definite and rank deficient, then the unique solution is composed of a number of Dirac masses equal to the rank of the matrix, all of which have nonnegative (respectively nonpositive) weights. Finally, in cases where (BPC) has multiple solutions, we demonstrate that there are infinitely many solutions composed of
K
c
+ 1 Dirac masses, with nonnegative (respectively nonpositive) weights if the matrix is positive (respectively negative) definite. |
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ISSN: | 0266-5611 1361-6420 |
DOI: | 10.1088/1361-6420/ac9998 |