2011, Rev. ed., Classics in applied mathematics, ISBN 9781611970722, Volume 66, xvi, 276

Book

ACM Transactions on Mathematical Software (TOMS), ISSN 0098-3500, 10/2015, Volume 41, Issue 4, pp. 1 - 20

Sparse matrix--matrix multiplication (SpGEMM) is a key operation in numerous areas from information to the physical sciences...

Parallel | matrix--matrix | sparse, GPU | Sparse | GPU | Matrix-matrix | COMPUTER SCIENCE, SOFTWARE ENGINEERING | MATHEMATICS, APPLIED | Algorithms | PARALLELISM | sparse | IMPLEMENTATION | Performance | matrix-matrix | Analysis | Graphics coprocessors | Matrices | Multiplication | Graphics processing units | Software | Processors | Programmers | Optimization | Computer programs | Sorting

Parallel | matrix--matrix | sparse, GPU | Sparse | GPU | Matrix-matrix | COMPUTER SCIENCE, SOFTWARE ENGINEERING | MATHEMATICS, APPLIED | Algorithms | PARALLELISM | sparse | IMPLEMENTATION | Performance | matrix-matrix | Analysis | Graphics coprocessors | Matrices | Multiplication | Graphics processing units | Software | Processors | Programmers | Optimization | Computer programs | Sorting

Journal Article

2015, ISBN 9789814667968, xii, 582 pages

Book

2010, ISBN 9780521119139, Volume 9780521119139, xvii, 316 p., [16] p. of plates

"Presenting the state of the art in sparse and multiscale image and signal processing, this book weds theory and practice to examine their applications in a...

Signal processing | Transformations (Mathematics) | Wavelets (Mathematics) | Image processing | Sparse matrices | Image Processing | Computer Science

Signal processing | Transformations (Mathematics) | Wavelets (Mathematics) | Image processing | Sparse matrices | Image Processing | Computer Science

Book

Journal of parallel and distributed computing, ISSN 0743-7315, 11/2015, Volume 85, pp. 47 - 61

General sparse matrix–matrix multiplication (SpGEMM) is a fundamental building block for numerous applications such as algebraic multigrid method (AMG...

Parallel algorithm | Merging | Linear algebra | Sparse matrix | Heterogeneous processor | Sparse matrix-matrix multiplication | GPU | PERFORMANCE | IMPLEMENTATION | COMPUTER SCIENCE, THEORY & METHODS | ALGORITHMS | Algorithms | Algebra | Distributed processing | Multiplication | Construction | Searching | Inserts | Load balancing | Processors

Parallel algorithm | Merging | Linear algebra | Sparse matrix | Heterogeneous processor | Sparse matrix-matrix multiplication | GPU | PERFORMANCE | IMPLEMENTATION | COMPUTER SCIENCE, THEORY & METHODS | ALGORITHMS | Algorithms | Algebra | Distributed processing | Multiplication | Construction | Searching | Inserts | Load balancing | Processors

Journal Article

IEEE transactions on information theory, ISSN 1557-9654, 2015, Volume 61, Issue 5, pp. 2909 - 2923

This paper considers the matrix completion problem. We show that it is not necessary to assume joint incoherence, which is a standard but unintuitive and restrictive condition that is imposed by previous studies...

Vectors | Complexity theory | Matrix decomposition | Sparse matrices | Joints | Standards | Information theory | incoherence | robust PCA | computational barrier | nuclear norm minimization | Matrix completion | DECOMPOSITION | COMPUTER SCIENCE, INFORMATION SYSTEMS | CLIQUES | ENGINEERING, ELECTRICAL & ELECTRONIC | Matrices | Algorithms | Research | Mathematical research

Vectors | Complexity theory | Matrix decomposition | Sparse matrices | Joints | Standards | Information theory | incoherence | robust PCA | computational barrier | nuclear norm minimization | Matrix completion | DECOMPOSITION | COMPUTER SCIENCE, INFORMATION SYSTEMS | CLIQUES | ENGINEERING, ELECTRICAL & ELECTRONIC | Matrices | Algorithms | Research | Mathematical research

Journal Article

Proceedings of the IEEE, ISSN 1558-2256, 2016, Volume 104, Issue 2, pp. 310 - 331

.... In this article, we first provide a brief review of existing matrix-based (two-way) component analysis methods for the joint analysis of such data with a focus on biomedical applications...

data fusion | nonnegative/sparse matrix/tensor factorizations | (multilinear) independent component analysis | Blind source separation | Matrix decomposition | Data mining | Analysis of multirelational data | Tensile stress | independent vector analysis (IVA) | Feature extraction | constrained Tucker decompositions for multiblock data | CP (CANDECOMP/PARAFAC) decompositions | (multiway) blind source separation (BSS) | group and joint independent component analysis | Bioinformatics | Principal component analysis | Biomedical signal processing | UNDERDETERMINED MIXTURES | BLIND SOURCE SEPARATION | FMRI DATA | NONNEGATIVE MATRIX | INDEPENDENT VECTOR ANALYSIS | ENGINEERING, ELECTRICAL & ELECTRONIC | TUCKER DECOMPOSITIONS | POLYADIC DECOMPOSITION | FUNCTIONAL MRI DATA | CANONICAL CORRELATION-ANALYSIS | Finite element analysis | Availability | Surgical implants | Biomedical materials | Tensors | Mathematical analysis | Biomedical data | Joints

data fusion | nonnegative/sparse matrix/tensor factorizations | (multilinear) independent component analysis | Blind source separation | Matrix decomposition | Data mining | Analysis of multirelational data | Tensile stress | independent vector analysis (IVA) | Feature extraction | constrained Tucker decompositions for multiblock data | CP (CANDECOMP/PARAFAC) decompositions | (multiway) blind source separation (BSS) | group and joint independent component analysis | Bioinformatics | Principal component analysis | Biomedical signal processing | UNDERDETERMINED MIXTURES | BLIND SOURCE SEPARATION | FMRI DATA | NONNEGATIVE MATRIX | INDEPENDENT VECTOR ANALYSIS | ENGINEERING, ELECTRICAL & ELECTRONIC | TUCKER DECOMPOSITIONS | POLYADIC DECOMPOSITION | FUNCTIONAL MRI DATA | CANONICAL CORRELATION-ANALYSIS | Finite element analysis | Availability | Surgical implants | Biomedical materials | Tensors | Mathematical analysis | Biomedical data | Joints

Journal Article

IEEE transactions on signal processing, ISSN 1941-0476, 2012, Volume 60, Issue 1, pp. 139 - 154

This paper introduces a new framework to construct fast and efficient sensing matrices for practical compressive sensing, called Structurally Random Matrix (SRM...

Transforms | Coherence | fast and efficient algorithm | Sensors | Random variables | sparse reconstruction | Sparse matrices | Compressed sensing | compressive sensing | random projection | Convergence | PURSUIT | UNCERTAINTY PRINCIPLES | RECONSTRUCTION | SIGNAL RECOVERY | ENGINEERING, ELECTRICAL & ELECTRONIC | Fading channels | Measurement | Technology application | Usage | Frequency modulation | Innovations | Signal processing | Hessian matrices | Simulation methods

Transforms | Coherence | fast and efficient algorithm | Sensors | Random variables | sparse reconstruction | Sparse matrices | Compressed sensing | compressive sensing | random projection | Convergence | PURSUIT | UNCERTAINTY PRINCIPLES | RECONSTRUCTION | SIGNAL RECOVERY | ENGINEERING, ELECTRICAL & ELECTRONIC | Fading channels | Measurement | Technology application | Usage | Frequency modulation | Innovations | Signal processing | Hessian matrices | Simulation methods

Journal Article

IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 04/2017, Volume 39, Issue 4, pp. 818 - 832

Low-rank recovery models have shown potential for salient object detection, where a matrix is decomposed into a low-rank matrix representing image background and a sparse matrix identifying salient objects...

subspace learning | Image segmentation | Laplace equations | Image color analysis | low rank | Computational modeling | Object detection | Matrix decomposition | Sparse matrices | Salient object detection | structured sparsity | matrix decomposition | VISUAL-ATTENTION | REGION DETECTION | MODEL | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Harmonic functions | Usage | Computer-generated environments | Computer simulation | State of the art | Sparsity | Salience | Decomposition | Performance measurement | Object recognition | Regularization | Vision systems | Image detection

subspace learning | Image segmentation | Laplace equations | Image color analysis | low rank | Computational modeling | Object detection | Matrix decomposition | Sparse matrices | Salient object detection | structured sparsity | matrix decomposition | VISUAL-ATTENTION | REGION DETECTION | MODEL | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Harmonic functions | Usage | Computer-generated environments | Computer simulation | State of the art | Sparsity | Salience | Decomposition | Performance measurement | Object recognition | Regularization | Vision systems | Image detection

Journal Article

ACM Transactions on Mathematical Software (TOMS), ISSN 0098-3500, 11/2011, Volume 38, Issue 1, pp. 1 - 25

We describe the University of Florida Sparse Matrix Collection, a large and actively growing set of sparse matrices that arise in real applications...

Graph drawing | performance evaluation | multilevel algorithms | sparse matrices | Performance evaluation | Sparse Matrices | Multilevel algorithms | MATHEMATICS, APPLIED | DESIGN | ALGORITHM | SIMULATION | COMPUTER SCIENCE, SOFTWARE ENGINEERING | SCHEME | Algorithms | CHOLESKY FACTORIZATION | Experimentation | Performance | Thermodynamics | Electromagnetism | Algebra | Fluid dynamics | Analysis | Online searching | Graphics software | Models | Internet/Web search services | Universities and colleges | Database searching | Collection

Graph drawing | performance evaluation | multilevel algorithms | sparse matrices | Performance evaluation | Sparse Matrices | Multilevel algorithms | MATHEMATICS, APPLIED | DESIGN | ALGORITHM | SIMULATION | COMPUTER SCIENCE, SOFTWARE ENGINEERING | SCHEME | Algorithms | CHOLESKY FACTORIZATION | Experimentation | Performance | Thermodynamics | Electromagnetism | Algebra | Fluid dynamics | Analysis | Online searching | Graphics software | Models | Internet/Web search services | Universities and colleges | Database searching | Collection

Journal Article

IEEE Transactions on Information Theory, ISSN 0018-9448, 11/2010, Volume 56, Issue 11, pp. 5862 - 5875

Compressed sensing (CS) has recently emerged as a powerful signal acquisition paradigm. In essence, CS enables the recovery of high-dimensional sparse signals...

wireless communications | compressed sensing | Estimation | Vectors | Hankel matrices | Sparse matrices | Training | Wireless communication | restricted isometry property | Channel estimation | sparse channel estimation | Random variables | Circulant matrices | Toeplitz matrices | COMPUTER SCIENCE, INFORMATION SYSTEMS | SIGNAL RECONSTRUCTION | ENGINEERING, ELECTRICAL & ELECTRONIC | RECOVERY | SYSTEMS | DICTIONARIES | Technology application | Communications circuits | Usage | Mobile communication systems | Wireless communication systems | Data compression | Estimation theory | Matrices | Methods | Statistical sampling | Reconstruction | Mathematical analysis | Impulse response | Vectors (mathematics) | Detection | Compressed | Channels | Information theory

wireless communications | compressed sensing | Estimation | Vectors | Hankel matrices | Sparse matrices | Training | Wireless communication | restricted isometry property | Channel estimation | sparse channel estimation | Random variables | Circulant matrices | Toeplitz matrices | COMPUTER SCIENCE, INFORMATION SYSTEMS | SIGNAL RECONSTRUCTION | ENGINEERING, ELECTRICAL & ELECTRONIC | RECOVERY | SYSTEMS | DICTIONARIES | Technology application | Communications circuits | Usage | Mobile communication systems | Wireless communication systems | Data compression | Estimation theory | Matrices | Methods | Statistical sampling | Reconstruction | Mathematical analysis | Impulse response | Vectors (mathematics) | Detection | Compressed | Channels | Information theory

Journal Article

The Annals of statistics, ISSN 0090-5364, 2011, Volume 39, Issue 2, pp. 887 - 930

... × T -matrix A corrupted by noise. We are particularly interested in the high-dimensional setting where the number mT of unknown entries can be much larger than the sample size N...

Integers | Minimax | Sample size | Analytical estimating | Matrices | Entropy | Random variables | Regression analysis | Covariance matrices | Estimators | Sparse recovery | Empirical process | Quasi-convex Schatten class embeddings | Schatten norm | Penalized least-squares estimator | High-dimensional low-rank matrices | CONSISTENCY | penalized least-squares estimator | empirical process | quasi-convex Schatten class embeddings | sparse recovery | STATISTICS & PROBABILITY | TRACE-NORM | SELECTION | AGGREGATION | ENTROPY | Probability | Mathematics | 62G05 | 62F10

Integers | Minimax | Sample size | Analytical estimating | Matrices | Entropy | Random variables | Regression analysis | Covariance matrices | Estimators | Sparse recovery | Empirical process | Quasi-convex Schatten class embeddings | Schatten norm | Penalized least-squares estimator | High-dimensional low-rank matrices | CONSISTENCY | penalized least-squares estimator | empirical process | quasi-convex Schatten class embeddings | sparse recovery | STATISTICS & PROBABILITY | TRACE-NORM | SELECTION | AGGREGATION | ENTROPY | Probability | Mathematics | 62G05 | 62F10

Journal Article

Pattern recognition, ISSN 0031-3203, 04/2018, Volume 76, pp. 715 - 726

•We propose a novel matrix classifier to simultaneously leverage the structural information within matrices and select useful features...

Matrix analysis | Sparse | Support vector machine | Low rank | Classification | REGRESSION | ALGORITHM | REPRESENTATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC

Matrix analysis | Sparse | Support vector machine | Low rank | Classification | REGRESSION | ALGORITHM | REPRESENTATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC

Journal Article

The Journal of Supercomputing, ISSN 0920-8542, 9/2016, Volume 72, Issue 9, pp. 3366 - 3386

A wide range of applications in engineering and scientific computing are based on the sparse matrix computation...

Processor Architectures | Programming Languages, Compilers, Interpreters | Sparse matrix representation | Computer Science | Matrix–vector multiplication | Adaptive strategy | Computer Science, general | GPU | Matrix-vector multiplication | SPMV | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | OPTIMIZATION | COMPUTER SCIENCE, THEORY & METHODS | ENGINEERING, ELECTRICAL & ELECTRONIC | Computer science | Electric properties

Processor Architectures | Programming Languages, Compilers, Interpreters | Sparse matrix representation | Computer Science | Matrix–vector multiplication | Adaptive strategy | Computer Science, general | GPU | Matrix-vector multiplication | SPMV | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | OPTIMIZATION | COMPUTER SCIENCE, THEORY & METHODS | ENGINEERING, ELECTRICAL & ELECTRONIC | Computer science | Electric properties

Journal Article

IEEE transactions on information theory, ISSN 0018-9448, 2010, Volume 56, Issue 6, pp. 2980 - 2998

Let M be an n¿ × n matrix of rank r, and assume that a uniformly random subset E of its entries is observed...

spectral methods | low rank | manifold optimization | matrix completion | Optimization methods | Reconstruction algorithms | Watches | Information filtering | Sparse matrices | Root mean square | Gradient descent | Collaboration | Motion pictures | Information filters | phase transition | Mathematical model | Low rank | Spectral methods | Manifold optimization | Matrix completion | Phase transition | APPROXIMATIONS | INFORMATION | COMPUTER SCIENCE, INFORMATION SYSTEMS | LOW-RANK MATRIX | ALGORITHMS | ENGINEERING, ELECTRICAL & ELECTRONIC | Matrices | Research | Reconstruction | Mean square values | Algorithms | Mathematical analysis | Data sets | Roots | Information theory

spectral methods | low rank | manifold optimization | matrix completion | Optimization methods | Reconstruction algorithms | Watches | Information filtering | Sparse matrices | Root mean square | Gradient descent | Collaboration | Motion pictures | Information filters | phase transition | Mathematical model | Low rank | Spectral methods | Manifold optimization | Matrix completion | Phase transition | APPROXIMATIONS | INFORMATION | COMPUTER SCIENCE, INFORMATION SYSTEMS | LOW-RANK MATRIX | ALGORITHMS | ENGINEERING, ELECTRICAL & ELECTRONIC | Matrices | Research | Reconstruction | Mean square values | Algorithms | Mathematical analysis | Data sets | Roots | Information theory

Journal Article

16.
Full Text
Parallel sparse matrix-matrix multiplication and indexing: Implementation and experiments

SIAM Journal on Scientific Computing, ISSN 1064-8275, 2012, Volume 34, Issue 4, pp. C170 - C191

Generalized sparse matrix-matrix multiplication (or SpGEMM) is a key primitive for many high performance graph algorithms as well as for some linear solvers, such as algebraic multigrid...

Graph batch update | Hypersparsity | Sparse matrix indexing | Numerical linear algebra | Two-dimensional data decomposition | Parallel computing | SpGEMM | Subgraph extraction | Sparse SUMMA | Graph algorithms | Sparse matrix-matrix multiplication | Graph contraction | Sparse matrix assignment | MATHEMATICS, APPLIED | two-dimensional data decomposition | DESIGN | sparse matrix-matrix multiplication | hypersparsity | BLAS | sparse SUMMA | sparse matrix assignment | sparse matrix indexing | numerical linear algebra | graph contraction | subgraph extraction | parallel computing | COMMUNICATION | graph algorithms | graph batch update | Multiplication | Algorithms | Scaling up | Blocking | Solvers | Serials | Processors | Indexing

Graph batch update | Hypersparsity | Sparse matrix indexing | Numerical linear algebra | Two-dimensional data decomposition | Parallel computing | SpGEMM | Subgraph extraction | Sparse SUMMA | Graph algorithms | Sparse matrix-matrix multiplication | Graph contraction | Sparse matrix assignment | MATHEMATICS, APPLIED | two-dimensional data decomposition | DESIGN | sparse matrix-matrix multiplication | hypersparsity | BLAS | sparse SUMMA | sparse matrix assignment | sparse matrix indexing | numerical linear algebra | graph contraction | subgraph extraction | parallel computing | COMMUNICATION | graph algorithms | graph batch update | Multiplication | Algorithms | Scaling up | Blocking | Solvers | Serials | Processors | Indexing

Journal Article

IEEE transactions on signal processing, ISSN 1941-0476, 2011, Volume 59, Issue 11, pp. 5338 - 5352

...), a receive node compresses its received signal via a linear transformation, referred to as a measurement matrix...

multiple-input multiple-output (MIMO) radar | direction of arrival (DOA) estimation | Symmetric matrices | Compressive sensing | MIMO radar | Transmitting antennas | Receiving antennas | Coherence | measurement matrix | Sensors | Sparse matrices | SIGNAL RECOVERY | ENGINEERING, ELECTRICAL & ELECTRONIC | Signal processing | Usage | Random noise theory | Simulation methods | Innovations | MIMO communications | Studies | Radar | Waveforms | Gaussian | Matrices | Criteria | Detection | Position (location) | Optimization

multiple-input multiple-output (MIMO) radar | direction of arrival (DOA) estimation | Symmetric matrices | Compressive sensing | MIMO radar | Transmitting antennas | Receiving antennas | Coherence | measurement matrix | Sensors | Sparse matrices | SIGNAL RECOVERY | ENGINEERING, ELECTRICAL & ELECTRONIC | Signal processing | Usage | Random noise theory | Simulation methods | Innovations | MIMO communications | Studies | Radar | Waveforms | Gaussian | Matrices | Criteria | Detection | Position (location) | Optimization

Journal Article

Proceedings of the National Academy of Sciences - PNAS, ISSN 0027-8424, 1/2013, Volume 110, Issue 4, pp. 1181 - 1186

In compressed sensing, one takes n < N samples of an N-dimensional vector x₀ using an n × V matrix A obtaining undersampled measurements y...

Tanneries | Polytopes | Determinism | Zero | Algorithms | Experimentation | Matrices | Mathematics | Animal vocalization | Universality | restricted isometry property | coherence | UNCERTAINTY PRINCIPLES | MULTIDISCIPLINARY SCIENCES | sparse recovery | POLYTOPES | universality in random matrix theory equiangular tight frames | NEIGHBORLINESS | Research | Vector spaces | Gaussian processes | Physical Sciences

Tanneries | Polytopes | Determinism | Zero | Algorithms | Experimentation | Matrices | Mathematics | Animal vocalization | Universality | restricted isometry property | coherence | UNCERTAINTY PRINCIPLES | MULTIDISCIPLINARY SCIENCES | sparse recovery | POLYTOPES | universality in random matrix theory equiangular tight frames | NEIGHBORLINESS | Research | Vector spaces | Gaussian processes | Physical Sciences

Journal Article