AbstractIn this paper, it is proved that every s-sparse vector x∈Rn can be exactly recovered from the measurement vector z=Ax∈Rm via some ℓq-minimization with 0<q⩽1, as soon as each s-sparse vector x∈Rn is uniquely determined by the measurement z. Moreover it is shown that the exponent q in the ℓq-minimization can be so chosen to be about 0.6796×(1−δ2s(A)), where δ2s(A) is the restricted isometry constant of order 2s for the measurement matrix A
In this correspondence, we introduce a sparse approximation property of order for a measurement matr...
This paper considers compressed sensing and affine rank minimization in both noiseless and noisy cas...
Abstract The joint sparse recovery problem is a generalization of the single measurement vector prob...
In this paper, it is proved that every s-sparse vector x is an element of R-n can be exactly recover...
AbstractIt is proved that every s-sparse vector x∈CN can be recovered from the measurement vector y=...
We consider the problem of recovering sparse vectors from underdetermined linear measurements via ℓ ...
In this correspondence, we introduce a sparse approximation property of order s for a measurement ma...
We consider the problem of recovering a sparse signal from underdetermined measurements when we have...
The purpose of this paper is to give a brief overview of the main results for sparse recovery via L ...
We investigate conditions under which the solution of an underdetermined linear system with minimal ...
International audienceWe propose novel necessary and sufficient conditions for a sensing matrix to b...
This paper establishes a sharp condition on the restricted isometry property (RIP) for both the spar...
AbstractIn this paper, it is proved that every s-sparse vector x∈Rn can be exactly recovered from th...
Abstract This paper focuses on the sufficient condition of block sparse recovery with the l 2 / l 1 ...
We propose a new algorithm to recover a sparse signal from a system of linear measurements. By proje...
In this correspondence, we introduce a sparse approximation property of order for a measurement matr...
This paper considers compressed sensing and affine rank minimization in both noiseless and noisy cas...
Abstract The joint sparse recovery problem is a generalization of the single measurement vector prob...
In this paper, it is proved that every s-sparse vector x is an element of R-n can be exactly recover...
AbstractIt is proved that every s-sparse vector x∈CN can be recovered from the measurement vector y=...
We consider the problem of recovering sparse vectors from underdetermined linear measurements via ℓ ...
In this correspondence, we introduce a sparse approximation property of order s for a measurement ma...
We consider the problem of recovering a sparse signal from underdetermined measurements when we have...
The purpose of this paper is to give a brief overview of the main results for sparse recovery via L ...
We investigate conditions under which the solution of an underdetermined linear system with minimal ...
International audienceWe propose novel necessary and sufficient conditions for a sensing matrix to b...
This paper establishes a sharp condition on the restricted isometry property (RIP) for both the spar...
AbstractIn this paper, it is proved that every s-sparse vector x∈Rn can be exactly recovered from th...
Abstract This paper focuses on the sufficient condition of block sparse recovery with the l 2 / l 1 ...
We propose a new algorithm to recover a sparse signal from a system of linear measurements. By proje...
In this correspondence, we introduce a sparse approximation property of order for a measurement matr...
This paper considers compressed sensing and affine rank minimization in both noiseless and noisy cas...
Abstract The joint sparse recovery problem is a generalization of the single measurement vector prob...
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