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Identification of Matrices Having a Sparse Representation [PDF]

IEEE Transactions on Signal Processing, 2008
We consider the problem of recovering a matrix from its action on a known vector in the setting where the matrix can be represented efficiently in a known matrix dictionary. Connections with sparse signal recovery allows for the use of efficient reconstruction techniques such as Basis Pursuit.
Götz E. Pfander, Holger Rauhut, Jared Tanner   +2 more
openaire   +7 more sources

Spectra of sparse random matrices [PDF]

Journal of Physics A: Mathematical and Theoretical, 2008
22 papges, 8 figures (one on graph-Laplacians added), one reference added, some typos ...
openaire   +3 more sources

The Rank Distribution of Sparse Random Linear Network Coding

IEEE Access, 2019
Sparse random linear network coding (SRLNC) is a promising solution for reducing the complexity of random linear network coding (RLNC). RLNC can be modeled as a linear operator channel (LOC).
Wenlin Chen, Fang Lu, Yan Dong
doaj   +1 more source

A constructive bandwidth reduction algorithm—A variant of GPS algorithm

AKCE International Journal of Graphs and Combinatorics, 2016
In this paper, a new viable bandwidth reduction algorithm for reducing the bandwidth of sparse symmetric matrices, is described. The proposed algorithm provides a reliable procedure to reduce the bandwidth and can easily be applied to the sparse ...
L. Jones Tarcius Doss, P. Arathi
doaj   +1 more source

Good Pivots for Small Sparse Matrices [PDF]

, 2020
11 ...
Manuel Kauers, Jakob Moosbauer
openaire   +3 more sources

Quantum circuits from non-unitary sparse binary matrices

Scientific Reports
Quantum computing leverages unitary matrices to perform reversible computations while preserving probability norms. However, many real-world applications involve non-unitary sparse matrices, posing a challenge for quantum implementation.
Krishnageetha Karuppasamy, Varunteja Puram, K. M. George, Thomas P. Johnson   +3 more
doaj   +1 more source

bspcov: An R Package for Bayesian sparse covariance matrix estimation

SoftwareX
The bspcov R package provides a Bayesian inference for covariance matrices. The bspcov is developed to aid in research that involves estimating constrained covariance matrices by enabling the use of state-of-the-art Bayesian inference methods.
Kyeongwon Lee, Kyoungjae Lee, Seongil Jo, Kwangmin Lee   +3 more
doaj   +1 more source

Sparse Matrices in MATLAB: Design and Implementation [PDF]

SIAM Journal on Matrix Analysis and Applications, 1992
MATLAB is an interactive environment and programming language for numerical computation. This paper describes the addition of sparse matrix storage and operations to MATLAB. This has not added any new functionality but has made existing programs more efficient in terms of storage and time. The authors concentrate on elementary sparse matrix operations,
John R. Gilbert, Cleve Moler, Robert Schreiber   +2 more
openaire   +2 more sources

Matrix Depot: an extensible test matrix collection for Julia [PDF]

PeerJ Computer Science, 2016
Matrix Depot is a Julia software package that provides easy access to a large and diverse collection of test matrices. Its novelty is threefold. First, it is extensible by the user, and so can be adapted to include the user’s own test problems.
Weijian Zhang, Nicholas J. Higham
doaj   +2 more sources

Sparse Recovery Using Sparse Random Matrices [PDF]

, 2010
Over the recent years, a new linear method for compressing high-dimensional data (e.g., images) has been discovered. For any high-dimensional vector x, its sketch is equal to Ax, where A is an m×n matrix (possibly chosen at random). Although typically the sketch length m is much smaller than the number of dimensions n, the sketch contains enough ...
openaire   +1 more source