A vector with at most k nonzeros is called k-sparse. We show that enumerating the support vectors of k-sparse solutions to a system Ax=b of r-sparse linear equations (i.e., where the rows of A are r-sparse) is fixed-parameter tractable (FPT) in the combined parameter r,k. For r=2 the problem is simple. For 0,1-matrices A we can also compute akernel. For systems of linear inequalities we get an FPT result in the combined parameter d,k, where d is the total number of minimal solutions. This is achieved by interpreting the problem as a case of group testing in the complex model. The problems stem from the reconstruction of chemical mixtures by observable reaction products
International audienceLet A be an nxm matrix with m>n, and suppose that the underdetermined linear s...
This paper deals with sparse phase retrieval, i.e., the problem of estimating a vector from quadrati...
Let A be a matrix of size N × M (a dictionary) and let ‖ · ‖ be a norm on N. For any data d ∈ N, w...
A vector with at most k nonzeros is called k-sparse. We show that enumerating the support vectors of...
We consider the problem of approximate solution ex of a linear system Ax = b over the reals, such th...
We give a new theoretical tool to solve sparse systems with finitely many solutions. It is based on ...
We study the thresholds for the property of containing a solution to a linear homogeneous system in ...
In an overdetermined and feasible system of linear equations Ax=b, let vector b be corrupted, in the...
AbstractWe consider the problem of approximate solution x̄ of of a linear system Ax = b over the rea...
SIGLEAvailable from British Library Document Supply Centre- DSC:4335.26205(HPL--92-167) / BLDSC - Br...
When piecewise-linear homotopy algorithms are applied to the problem of approximating a zero of a sp...
Abstract—Recently, the worse-case analysis, probabilistic anal-ysis and empirical justification have...
An over view of advanced techniques for solving large sparse linear systems of equations is presente...
Group-based sparsity models are proven instrumental in linear regression problems for recovering sig...
We consider inexact linear equations y ≈ Φα where y is a given vector in R n, Φ is a given n by m ma...
International audienceLet A be an nxm matrix with m>n, and suppose that the underdetermined linear s...
This paper deals with sparse phase retrieval, i.e., the problem of estimating a vector from quadrati...
Let A be a matrix of size N × M (a dictionary) and let ‖ · ‖ be a norm on N. For any data d ∈ N, w...
A vector with at most k nonzeros is called k-sparse. We show that enumerating the support vectors of...
We consider the problem of approximate solution ex of a linear system Ax = b over the reals, such th...
We give a new theoretical tool to solve sparse systems with finitely many solutions. It is based on ...
We study the thresholds for the property of containing a solution to a linear homogeneous system in ...
In an overdetermined and feasible system of linear equations Ax=b, let vector b be corrupted, in the...
AbstractWe consider the problem of approximate solution x̄ of of a linear system Ax = b over the rea...
SIGLEAvailable from British Library Document Supply Centre- DSC:4335.26205(HPL--92-167) / BLDSC - Br...
When piecewise-linear homotopy algorithms are applied to the problem of approximating a zero of a sp...
Abstract—Recently, the worse-case analysis, probabilistic anal-ysis and empirical justification have...
An over view of advanced techniques for solving large sparse linear systems of equations is presente...
Group-based sparsity models are proven instrumental in linear regression problems for recovering sig...
We consider inexact linear equations y ≈ Φα where y is a given vector in R n, Φ is a given n by m ma...
International audienceLet A be an nxm matrix with m>n, and suppose that the underdetermined linear s...
This paper deals with sparse phase retrieval, i.e., the problem of estimating a vector from quadrati...
Let A be a matrix of size N × M (a dictionary) and let ‖ · ‖ be a norm on N. For any data d ∈ N, w...