摘要: We describe a simple random-sampling based procedure for producing sparse matrix approximations. Our procedure and analysis are extremely simple: the analysis uses nothing more than the Chernoff-HoeffDOI: 10.1007/11830924_26 被引量: 184 ...
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
而核函数值的计算最终都需要去做内积运算,这就意味着原始空间的维度很高会增加内积运算的时间;对于dense matrix我就直接用numpy的dot了,而sparse matrix采用的是CSR表示法,求它的内积我实验过的方法有三种,第一种不需要额外空间,但时间复杂度为O(nlgn),第二种需要一个hash表(用dictionary代替了),时间复杂度为线性...
C Paige,Sanders M.A.摘要: 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 ...
An “industrial strength” algorithm for solving sparse symmetric generalized eigenproblems is described. The algorithm has its foundations in known techni... RG Grimes,JG Lewis,HD Simon - 《Siam Journal on Matrix Analysis & Applications》 被引量: 608发表: 1994年 The Lanczos algorithm with partial...
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...
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 ...
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. ...
In their full generality, Good's methods are applicable to certain problems in which one must multiply an N-vector by an N X N matrix which can be factored into m sparse matrices, where m is proportional to log N. This results inma procedure requiring a number of operations proportional ...
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...