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Everything about Sparse matrix
Matrix in which most of the elements are zero
In numerical analysis and scientific computing, a sparse matrix or sparse array is a matrix in which most of the elements are zero. There is no strict definition regarding the proportion of zero-value elements for a matrix to qualify as sparse but a common criterion is that the number of non-zero elements is roughly equal to the number of rows or columns. By contrast, if most of the elements are non-zero, the matrix is considered dense. The number of zero-valued elements divided by the total number of elements is sometimes referred to as the sparsity of the matrix. Wikipedia →
🧠Start here: a 6-step reading path
- Read the overview: Sparse matrixA 5-minute orientation
- Skim a recent paper: A Systematic Survey of General Sparse Matrix-matrix MultiplicationInfluential work since 2022
- Pick up a classic book: Iterative solution of large sparse systems of equationsThe most-reprinted book on the subject
- Read the landmark paper: Sparse matrix test problemsCited 444 times
- Go sideways: Band matrixA closely related topic
- Go sideways: Skyline matrixA closely related topic
đź“– Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Sparse matrix test problems
A Sparse â„‹-Matrix Arithmetic.
Sparse estimation of a covariance matrix
Sparse matrix multiplication: The distributed block-compressed sparse row library
Fast sparse matrix multiplication
Robust Matrix Decomposition With Sparse Corruptions
Sparse Support Matrix Machine
Optimizing Sparse Matrix—Matrix Multiplication for the GPU
🆕 Influential since 2022
A Systematic Survey of General Sparse Matrix-matrix Multiplication
Temporal knowledge graph embedding via sparse transfer matrix
Graph Regularized Sparse Non-Negative Matrix Factorization for Clustering
An Accelerator for Sparse Convolutional Neural Networks Leveraging Systolic General Matrix-matrix Multiplication
An Efficient Gustavson-Based Sparse Matrix–Matrix Multiplication Accelerator on Embedded FPGAs
đź“š Books worth reading
Iterative solution of large sparse systems of equationsWolfgang Hackbusch · 1993
Theoretical foundations and numerical methods for sparse recoveryMassimo Fornasier · 2010- 📕Modern algorithms for large sparse eigenvalue problemsArnd Meyer · 1987
Matrix DecompositionsAndrew Kloczkowski · 2022
Large sparse numerical optimizationThomas F. Coleman · 1984
Computer solution of large sparse positive definite systemsAlan George · 1981
Sparse matrix techniques, Copenhagen 1976Course in Advanced Sparse Matrix Techniques Technical University of Denmark 1976. · 1977
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
đź”— Related topics
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