In this paper, we survey algorithms for sparse recovery problems that are based on sparse random matrices. Such matrices has several attractive properties: they support algorithms with low computational complexity, and make it easy to perform incremental updates to signals. We discuss applications to several areas, including compressive sensing, data stream computing, and group testing.Statens naturvidenskabelige forskningsrad (Denmark)Center for Massive Data Algorithmics (MADALGO)David & Lucile Packard Foundation (Fellowship)National Science Foundation (U.S.) (grant CCF-0728645)National Science Foundation (U.S.) (grant CCF-0910765)National Science Foundation (U.S.) (grant DMS-0547744
In the context of the compressed sensing problem, we propose a new ensemble of sparse random matrice...
We consider the problem of recovering a matrix from its action on a known vector in the setting wher...
This paper considers compressed sensing and affine rank minimization in both noiseless and noisy cas...
Abstract. Compressive sensing predicts that sufficiently sparse vectors can be recovered from highly...
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/2...
Recall the setup in compressive sensing. There is an unknown signal z ∈ Rn, and we can only glean in...
Compressive sensing is a methodology for the reconstruction of sparse or compressible signals using ...
It is well known that `1 minimization can be used to recover sufficiently sparse unknown signals fro...
We propose an algorithm for recovering the matrix A in X = AS where X is a random vector of lower d...
Although many sparse recovery algorithms have been proposed recently in compressed sensing (CS), it ...
It is well known that the performance of sparse vector recovery algorithms from compressive measurem...
The purpose of this paper is to give a brief overview of the main results for sparse recovery via L ...
The problem of recovering sparse signals from a limited number of measurements is now ubiquitous in ...
The theory of compressed sensing shows that sparse signals in high-dimensional spaces can be recover...
These notes give a mathematical introduction to compressive sensing focusing on recovery using `1-mi...
In the context of the compressed sensing problem, we propose a new ensemble of sparse random matrice...
We consider the problem of recovering a matrix from its action on a known vector in the setting wher...
This paper considers compressed sensing and affine rank minimization in both noiseless and noisy cas...
Abstract. Compressive sensing predicts that sufficiently sparse vectors can be recovered from highly...
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/2...
Recall the setup in compressive sensing. There is an unknown signal z ∈ Rn, and we can only glean in...
Compressive sensing is a methodology for the reconstruction of sparse or compressible signals using ...
It is well known that `1 minimization can be used to recover sufficiently sparse unknown signals fro...
We propose an algorithm for recovering the matrix A in X = AS where X is a random vector of lower d...
Although many sparse recovery algorithms have been proposed recently in compressed sensing (CS), it ...
It is well known that the performance of sparse vector recovery algorithms from compressive measurem...
The purpose of this paper is to give a brief overview of the main results for sparse recovery via L ...
The problem of recovering sparse signals from a limited number of measurements is now ubiquitous in ...
The theory of compressed sensing shows that sparse signals in high-dimensional spaces can be recover...
These notes give a mathematical introduction to compressive sensing focusing on recovery using `1-mi...
In the context of the compressed sensing problem, we propose a new ensemble of sparse random matrice...
We consider the problem of recovering a matrix from its action on a known vector in the setting wher...
This paper considers compressed sensing and affine rank minimization in both noiseless and noisy cas...