Results 31 to 40 of about 1,040,614 (343)
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
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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
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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
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
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Convolutional compressed sensing using deterministic sequences [PDF]
, 2012 This is the author's accepted manuscript (with working title "Semi-universal convolutional compressed sensing using (nearly) perfect sequences"). The final published article is available from the link below. Copyright @ 2012 IEEE.
Cong Ling +4 more
core +2 more sources
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
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 +2 more sources
"Compress and eliminate" solver for symmetric positive definite sparse matrices [PDF]
SIAM Journal on Scientific Computing, 2016 We propose a new approximate factorization for solving linear systems with symmetric positive definite sparse matrices. In a nutshell the algorithm is to apply hierarchically block Gaussian elimination and additionally compress the fill-in.
D. Sushnikova, I. Oseledets
semanticscholar +1 more source
New Orthogonal Transforms for Signal and Image Processing
Applied Sciences, 2021 In the paper, orthogonal transforms based on proposed symmetric, orthogonal matrices are created. These transforms can be considered as generalized Walsh–Hadamard Transforms.
Andrzej Dziech
doaj +1 more source