Jacobi: The Jacobi Algorithm using Rcpp

In JacobiEigen: Classical Jacobi Eigenvalue Algorithm

Description

The Classical Jacobi Algorithm

Usage

Jacobi(x, symmetric = TRUE, only.values = FALSE, eps = 0)

Arguments

x	A real symmetric matrix
symmetric	a logical value. Is the matrix symmetric? (Only symmetric matrices are allowed.)
only.values	A logical value: do you want only the eigenvalues?
eps	an error tolerance. 0.0 implies .Machine$double.eps and sqrt(.Machine$double.eps) if only.values = TRUE

Details

Eigenvalues and optionally, eigenvectore, of a real symmetric matrix using the classical Jacobi algorithm, (Jacobi, 1854)

Value

a list of two components as for base::eigen

Examples

V <- crossprod(matrix(runif(40, -1, 1), 8))
Jacobi(V)
identical(Jacobi(V), JacobiR(V))
all.equal(Jacobi(V)$values, base::eigen(V)$values)

Example output

$values
[1] 3.8295873 2.9634209 2.1540343 1.7205096 0.9341117

$vectors
           [,1]        [,2]       [,3]        [,4]       [,5]
[1,]  0.5998277  0.05870473  0.4455547 -0.21073409  0.6275608
[2,]  0.5901163  0.40729883 -0.6672423 -0.11363479 -0.1665692
[3,] -0.5171593  0.63414430 -0.1869099 -0.09873786  0.5345304
[4,]  0.1098265 -0.16633551 -0.2468741  0.86976662  0.3779284
[5,] -0.1115966 -0.63313104 -0.5102810 -0.42004184  0.3871297

[1] TRUE
[1] TRUE

JacobiEigen documentation built on April 17, 2021, 9:06 a.m.