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Sparse matrix
Known as:
Dense matrix
, Sparse vector
, Sparsity
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In numerical analysis, a sparse matrix is a matrix in which most of the elements are zero. By contrast, if most of the elements are nonzero, then the…
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APMonitor
ARPACK
ASCEND
ASTAP
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Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2015
Highly Cited
2015
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
,
Jimmy Ba
ICLR
2015
Corpus ID: 6628106
We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive…
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Highly Cited
2011
Highly Cited
2011
The university of Florida sparse matrix collection
T. Davis
,
Yifan Hu
TOMS
2011
Corpus ID: 207191190
We describe the University of Florida Sparse Matrix Collection, a large and actively growing set of sparse matrices that arise in…
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Highly Cited
2011
Highly Cited
2011
Rank-Sparsity Incoherence for Matrix Decomposition
V. Chandrasekaran
,
S. Sanghavi
,
P. Parrilo
,
A. Willsky
SIAM J. Optim.
2011
Corpus ID: 1522297
Suppose we are given a matrix that is formed by adding an unknown sparse matrix to an unknown low-rank matrix. Our goal is to…
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Highly Cited
2010
Highly Cited
2010
Online Learning for Matrix Factorization and Sparse Coding
J. Mairal
,
F. Bach
,
J. Ponce
,
G. Sapiro
J. Mach. Learn. Res.
2010
Corpus ID: 556331
Sparse coding--that is, modelling data vectors as sparse linear combinations of basis elements--is widely used in machine…
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Highly Cited
2009
Highly Cited
2009
Implementing sparse matrix-vector multiplication on throughput-oriented processors
Nathan Bell
,
M. Garland
Proceedings of the Conference on High Performance…
2009
Corpus ID: 14531936
Sparse matrix-vector multiplication (SpMV) is of singular importance in sparse linear algebra. In contrast to the uniform…
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Highly Cited
2008
Highly Cited
2008
Ecient Sparse Matrix-Vector Multiplication on CUDA
Nathan Bell
,
M. Garland
2008
Corpus ID: 11725419
The massive parallelism of graphics processing units (GPUs) oers tremendous performance in many high-performance computing…
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Highly Cited
2007
Highly Cited
2007
Optimization of sparse matrix-vector multiplication on emerging multicore platforms
Samuel Williams
,
L. Oliker
,
R. Vuduc
,
J. Shalf
,
K. Yelick
,
J. Demmel
Proceedings of the ACM/IEEE Conference on…
2007
Corpus ID: 1845814
We are witnessing a dramatic change in computer architecture due to the multicore paradigm shift, as every electronic device from…
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Review
2005
Review
2005
OSKI: A Library of Automatically Tuned Sparse Matrix Kernels
R. Vuduc
,
J. Demmel
,
K. Yelick
2005
Corpus ID: 6252079
The Optimized Sparse Kernel Interface (OSKI) is a collection of low-level primitives that provide automatically tuned…
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Highly Cited
2004
Highly Cited
2004
Non-negative Matrix Factorization with Sparseness Constraints
P. Hoyer
J. Mach. Learn. Res.
2004
Corpus ID: 12009862
Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non…
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Highly Cited
1999
Highly Cited
1999
A Sparse Matrix Arithmetic Based on H-Matrices. Part I: Introduction to H-Matrices
W. Hackbusch
Computing
1999
Corpus ID: 15496936
A class of matrices (H-matrices) is introduced which have the following properties. (i) They are sparse in the sense that only…
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