On computing the inverse of a sparse matrix - Niessner, Reichert - 1983 () Citation Context ... e.g., TMS320C6416T (up to 8,000 MIPS). Besides, the matrices to of the elements are zero (in the above worst case, 92.9%). Inverting the matrices would be fast using sparse matrix ...
摘要: Publication » TECHNIQUES FOR SYNTHETIC INPUT/OUTPUT WORKLOAD GENERATION A Thesis submitted in partial satisfaction of the requirements for the degree of MASTER OF SCIENCE in COMPUTER ENGINEERING.DOI: 10.2307/2320513 被引量: 3 年份: 1981 ...
doi:10.1080/00207167108803052A linear iterative method for evaluating the Moore-Penrose generalized inverse of a matrix is described and conditions for optimizing its rate of convergence are givenR.P.StateTewarsonStateInformaworldInternational Journal of Computer Mathematics...
(2014)199–213indexlargestJordanblockbelongingDefinitions1.1Definition6.2.4].presentworkweconsidermatrixinversesquareroot,matrixfunctionhasapplications,e.g.,optimalsymmetricorthogonalizationgeneralizedeigenvalueprob-lemalsoappearsmatrixsignfunctionsign(A)whichcanwhicharisesalgebraicRiccatiequationsapplicationsfrom...
In this paper, an improved version of this method is presented for computing the pseudo inverse of an m脳n real matrix A with rank r>0. Numerical experiments show that the resulting pseudoinverse matrix is reasonably accurate and its computation time is significantly less than that obtained by...
The problem of computing an eigenvector of an inverse Monge matrix in max–plus algebra is addressed. For a general matrix, the problem can be solved in at most O ( n 3 ) time. This note presents an O ( n 2 ) algorithm for computing one max–plus algebraic eigenvector of an inverse...
function not implemented linalg.inv(a) Compute the (multiplicative) inverse of a matrix. linalg.pinv(a[, rcond]) Compute the (Moore-Penrose) pseudo-inverse of a matrix. function not implemented linalg.tensorinv(a[, ind]) Compute the ‘inverse’ of an N-dimensional array.Logic...
OpenCL Matrix Multiplication This sample implements matrix multiplication and is exactly the same as Chapter 6 of the programming guide. It has been written for clarity of exposition to illustrate various OpenCL programming principles, not with the goal of providing the most performant generic kernel...
where Wout is the output weight matrix and the subscript total indicates that it can be composed of constant, linear, and nonlinear terms as explained below. The standard approach, commonly used in the RC community, is to choose a nonlinear activation function such as f(x) = tanh(x) ...
outcome, which is supported by a necessary but not sufficient condition - imposed on the largest singular value of the effective stability matrixWγ = (1 − γ) + γW. Then, in RC output training, whose design commonly amounts to two aspects. One is to determine the ...