We revisit the asymptotic analysis of probabilistic construction of adjacency matrices of
expander graphs proposed in Bah and Tanner. With better bounds we derived a new …

We revisit the probabilistic construction of sparse random matrices where each column has
a fixed number of nonzeros whose row indices are drawn uniformly at random with …

Expander graphs have been recently proposed to construct efficient compressed sensing
algorithms. In particular, it has been shown that any $ n $-dimensional vector that is $ k …

We consider the problem of learning a graph modeling the statistical relations of the $ d $
variables of a dataset with $ n $ samples $ X\in\mathbb {R}^{n\times d} $. Standard …

In this paper, we propose a general framework for the asymptotic analysis of node-based
verification-based algorithms. In our analysis we tend the signal length $ n $ to infinity. We …

Linear sketching is a powerful tool for the problem of sparse signal recovery, having
numerous applications such as compressive sensing, data stream computing, graph …

Linear sketching and recovery of sparse vectors with randomly constructed sparse matrices
has numerous applications in several areas, including compressive sensing, data stream …

Expander graphs have been recently proposed to construct efficient compressed sensing
algorithms. In particular, it has been shown that any n-dimensional vector that is k-sparse …

Abstract Matrices Φ∈ ℝn× p satisfying the restricted isometry property (RIP) are an
important ingredient of the compressive sensing methods. While it is known that random …

This contributed volume contains articles written by the plenary and invited speakers from
the second international MATHEON Workshop 2015 that focus on applications of …