A.Schoor, Fast Algorithm for Sparse Matrix Multiplication, Information Processing Letters 15, No.2, 1982, 87-89Fast Algorithm for Sparse Matrix Multiplication - Schoor - 1982 () Citation Context ...pect to the sparsity structure. For multiplying an n \Theta n matrix A with non-zero density ...
TileSpMVis an open source code that uses a tiled structure to optimize sparse matrix-vector multiplication (SpMV) on GPUs. Paper information Yuyao Niu, Zhengyang Lu, Meichen Dong, Zhou Jin, Weifeng Liu and Guangming Tan, "TileSpMV: A Tiled Algorithm for Sparse Matrix-Vector Multiplication on ...
We describe an efficient implementation of an algorithm for computing selected elements of a general sparse symmetric matrix A that can be decomposed as A = LDLT where L is lower triangular and D is diagonal. Our implementation, which is called SelInv, is built on top of an efficient supermo...
When the matrix is sparse this method works fine because sparse matrices take less time to compute. It is not practically possible as it is computation and theoretical approach only. It takes more space for storing sub matrices. There is less chance of accuracy. Chat on Discord ...
Matrix decompositionSparse matricesA fast algorithm for the solution of a Toeplitz system of equations is presented. The algorithm requires order N(log N)$... R Kumar - 《IEEE Trans.acoust.speech Signal Processing》 被引量: 122发表: 1985年 Parallel finite-element tearing and interconnecting algor...
FE-1 is a simple algorithm based on basic pixel statistics (presented in “Detecting ants using motion-based foreground detection algorithms” section), and 3-term decomposition38 (dented as FE-2) is an algorithm based on low-rank matrix decomposition for foreground detection in videos. We ...
turning portions of the sparse matrix into dense blocks and invoking high-performance BLAS/lapack libraries. It is designed with optimization libraries for Levenberg-Marquardt in mind, and aims at reducing part of the complexity offering the best tool for the job. Compared to the library currently...
An iterative method is given for solving Ax ~ffi b and minU Ax- b 112, where the matrix A is large and sparse. The method is based on the bidiagonalization procedure of Golub and Kahan. It is analytically equivalent to the standard method of conjugate gradients, but possesses more favorable...
matrix. Furthermore, we apply the proposed algorithm to sparse systems arising from discretizations of the one-dimensional heat equation and the two-dimensional Poisson’s equation. Numerical simulations illustrate the capability and effectiveness of the proposed algorithm comparing to well-known methods ...
Other hybrid models merged filter and embedded algorithms. For example, a two-step sparselogistic regression modelwas developed (Algamal & Lee, 2019), which comprises the sure independence screening (SIS) algorithm and the adaptive least absolute shrinkage and selection algorithm (LASSO). First, SIS...