Results 31 to 40 of about 739,476 (283)
Linear Algebra and its Applications, 2003 The sparsity of orthogonal matrices which have both a column and a row of nonzero is studied. In Section 2, the authors describe a rich family \(n\) by \(n\) orthogonal matrices, namely, those that are the product of \(n-1\) Givens rotations. They show that this family contains a sparsest fully indecomposable orthogonal matrix with a full row.
Cheon, Gi-Sang +4 more
openaire +1 more source
Large-Scale Visualization of Sparse Matrices [PDF]
, 2014 An efficient algorithm for parallel acquisition of visualization data for large sparse matrices is presented and evaluated both analytically and empirically.
Tvrdik, P. +3 more
core +1 more source
SparseM: A Sparse Matrix Package for R *
Journal of Statistical Software, 2003 SparseM provides some basic R functionality for linear algebra with sparse matrices. Use of the package is illustrated by a family of linear model fitting functions that implement least squares methods for problems with sparse design matrices ...
Roger Koenker, Pin Ng
doaj +1 more source
Local Laws for Sparse Sample Covariance Matrices
Mathematics, 2022 We proved the local Marchenko–Pastur law for sparse sample covariance matrices that corresponded to rectangular observation matrices of order n×m with n/m→y (where y>0) and sparse probability npn>logβn (where β>0).
Alexander N. Tikhomirov +1 more
doaj +1 more source
Exhaustive Search for Various Types of MDS Matrices
IACR Transactions on Symmetric Cryptology, 2019 MDS matrices are used in the design of diffusion layers in many block ciphers and hash functions due to their optimal branch number. But MDS matrices, in general, have costly implementations. So in search for efficiently implementable MDS matrices, there
Abhishek Kesarwani +2 more
doaj +1 more source
New flexible deterministic compressive measurement matrix based on finite Galois field
IET Image Processing, 2022 Nowadays, the deterministic construction of sensing matrices is a hot topic in compressed sensing. The coherence of the measurement matrix is an important research area in the design of deterministic compressed sensing.
Vahdat Kazemi +2 more
doaj +1 more source
The rank of sparse random matrices [PDF]
Random Structures & Algorithms, 2020 AbstractWe determine the asymptotic normalized rank of a random matrix over an arbitrary field with prescribed numbers of nonzero entries in each row and column. As an application we obtain a formula for the rate of low‐density parity check codes. This formula vindicates a conjecture of Lelarge (2013).
Amin Coja-Oghlan +4 more
openaire +7 more sources
Combinatorial Regression and Improved Basis Pursuit for Sparse Estimation [PDF]
, 2012 Sparse representations accurately model many real-world data sets. Some form of sparsity is conceivable in almost every practical application, from image and video processing, to spectral sensing in radar detection, to bio-computation and genomic signal ...
Khajehnejad, M. Amin
core +1 more source
Sparse matrices in data analysis [PDF]
Computational Statistics, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nickolay T. Trendafilov +2 more
openaire +3 more sources
Journal of Telecommunications and Information Technology, 2019 This paper considers several algorithms for parallelizing the procedure of forward and back substitution for high-order symmetric sparse matrices on multi-core computers with shared memory.
Sergiy Fialko
doaj +1 more source