An algorithm was proposed, using sparse matrix as the storage structure of the data. To prevent the critical path from being lost, the queue method was adopted for the operation. Compared with the classical algorithm, this algorithm is simple, with close asymptotic time complexity (O (n e~2...
A new algorithm for reducing the bandwidth and profile of a sparse matrix is described. Extensive testing on finite element matrices indicates that the algorithm typically produces bandwidth and profile which are comparable to those of the commonly-used reverse Cuthill-McKee algorithm, yet requires sig...
Source code of the IPDPS '21 paper: "TileSpMV: A Tiled Algorithm for Sparse Matrix-Vector Multiplication on GPUs" by Yuyao Niu, Zhengyang Lu, Meichen Dong, Zhou Jin, Weifeng Liu, and Guangming Tan. - SuperScientificSoftwareLaboratory/TileSpMV
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. ...
We develop an algorithm for computing the symbolic Cholesky factorization of a large sparse symmetric positive definite matrix. The algorithm is intended for a message-passing multiprocessor system, such as the hypercube, and is based on the concept of elimination forest. In addition, we provide an...
Source code of the PPoPP '22 paper: "TileSpGEMM: A Tiled Algorithm for Parallel Sparse General Matrix-Matrix Multiplication on GPUs" by Yuyao Niu, Zhengyang Lu, Haonan Ji, Shuhui Song, Zhou Jin, and Weifeng Liu. - SuperScientificSoftwareLaboratory/TileSp
The proposed algorithm, which is a variation of a well known algorithm, uses the fact that TDMs are normally rectangular sparse matrices to reduce the computation time and also to achieve better accuracy than the original algorithm. In addition, all matrix products in the proposed algorithm are ...
311.Sparse-Matrix-Multiplication (M) 168.Excel-Sheet-Column-Title (H) 453.Minimum-Moves-to-Equal-Array-Elements (M) 782.Transform-to-Chessboard (H+) 466.Count-The-Repetitions (H) 810.Chalkboard-XOR-Game (H) 420.Strong-Password-Checker (H) 775.Global-and-Local-Inversions (M) 348.Design...
,xn], the goal of the KSVD and MOD algorithms is to find a dictionary B and a sparse matrix Γ that minimize the following representation error (B^,Γ^)=argminB,Γ‖X-BΓ‖F2subject to‖γi‖0⩽T0∀i, where γi represent the columns of Γ and T0 denotes the sparsity level. ...