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. We give different branching algorithms based on the close relationship to the hitting set problem in fixed-rank hypergraphs. For r=2 the problem is simple. For 0,1-matrices A we can also compute a kernel. 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 interpeting the problem as a case of group testing in the complex model. The problems stem from the reconstruction ...
AbstractA Las Vegas randomized algorithm for solving sparse linear systems over principal ideal doma...
AbstractWe consider systems of equations of the form AATx = b, where A is a sparse matrix having a s...
This paper deals with sparse phase retrieval, i.e., the problem of estimating a vector from quadrati...
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 ...
In an overdetermined and feasible system of linear equations Ax=b, let vector b be corrupted, in th...
When piecewise-linear homotopy algorithms are applied to the problem of approximating a zero of a sp...
AbstractWe consider the problem of approximate solution x̄ of of a linear system Ax = b over the rea...
Group-based sparsity models are proven instrumental in linear regression problems for recovering sig...
We study the thresholds for the property of containing a solution to a linear homogeneous system in ...
An over view of advanced techniques for solving large sparse linear systems of equations is presente...
A common theme in modern combinatorics consists in proving sparse analogues of results known in the ...
Let A be a matrix of size N × M (a dictionary) and let ‖ · ‖ be a norm on N. For any data d ∈ N, w...
Abstract—Recently, the worse-case analysis, probabilistic anal-ysis and empirical justification have...
AbstractA Las Vegas randomized algorithm for solving sparse linear systems over principal ideal doma...
AbstractWe consider systems of equations of the form AATx = b, where A is a sparse matrix having a s...
This paper deals with sparse phase retrieval, i.e., the problem of estimating a vector from quadrati...
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 ...
In an overdetermined and feasible system of linear equations Ax=b, let vector b be corrupted, in th...
When piecewise-linear homotopy algorithms are applied to the problem of approximating a zero of a sp...
AbstractWe consider the problem of approximate solution x̄ of of a linear system Ax = b over the rea...
Group-based sparsity models are proven instrumental in linear regression problems for recovering sig...
We study the thresholds for the property of containing a solution to a linear homogeneous system in ...
An over view of advanced techniques for solving large sparse linear systems of equations is presente...
A common theme in modern combinatorics consists in proving sparse analogues of results known in the ...
Let A be a matrix of size N × M (a dictionary) and let ‖ · ‖ be a norm on N. For any data d ∈ N, w...
Abstract—Recently, the worse-case analysis, probabilistic anal-ysis and empirical justification have...
AbstractA Las Vegas randomized algorithm for solving sparse linear systems over principal ideal doma...
AbstractWe consider systems of equations of the form AATx = b, where A is a sparse matrix having a s...
This paper deals with sparse phase retrieval, i.e., the problem of estimating a vector from quadrati...