A method is presented of calculating the inverse of a matrix whose elements are a linear combination of Walsh functions. Owing to an elegant property of Walsh functions, the inverse of such a matrix is shown to be obtained by solving a set of linear algebraic equations.doi:10.1080/...
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 ...
(2014)199–213indexlargestJordanblockbelongingDefinitions1.1Definition6.2.4].presentworkweconsidermatrixinversesquareroot,matrixfunctionhasapplications,e.g.,optimalsymmetricorthogonalizationgeneralizedeigenvalueprob-lemalsoappearsmatrixsignfunctionsign(A)whichcanwhicharisesalgebraicRiccatiequationsapplicationsfrom...
This paper introduces a recurrence formula for computing inverse matrix A~(-1) that derive itself from the solution of linear differential equations X(t)=AX(t) and finding the inverse of ei- genvalue matrix (SI-A)~(-1). When this method is applied to solve the n-order inverse matrix,...
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...
However, the algorithm uses randomly sampled matrices to define the underlying recurrent neural network and has a multitude of metaparameters that must be optimized. Recent results demonstrate the equivalence of reservoir computing to nonlinear vector autoregression, which requires no random matrices, ...
For example, a recurrent Hopfield neural network can be used to find the Moore-Penrose generalized inverse of a matrix, thus enabling a broad class of linear optimizations to be solved efficiently, at low energy cost. However, deploying numerical algorithms on hardware platforms that severely limit...
Roll-to-plate printable RGB achromatic metalens for wide-field-of-view holographic near-eye displays Using a topological inverse design process with finite-difference time-domain simulations, the authors fabricate high-numerical-aperture red, green and blue achromatic metalenses for compact near-eye di...
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...
Matrix multiplication Show 3 more Some familiarity with linear algebra is essential to understand quantum computing. This article introduces the basic concepts of linear algebra and how to work with vectors and matrices in quantum computing. Vectors A column vector, or vector for short, v of dime...