Dirk Eddelbuettel — written Jan 11, 2013 — source
A previous post showed how to compute eigenvalues using the Armadillo library via RcppArmadillo.
Here, we do the same using Eigen and the RcppEigen package.
We can illustrate this easily via a random sample matrix.
[1] 0.3319 1.6856 2.4099 14.2100
In comparison, R gets the same results (in reverse order) and also returns the eigenvectors.
$values [1] 14.2100 2.4099 1.6856 0.3319 $vectors [,1] [,2] [,3] [,4] [1,] 0.69988 -0.55799 0.4458 -0.00627 [2,] -0.06833 -0.08433 0.0157 0.99397 [3,] 0.44100 -0.15334 -0.8838 0.03127 [4,] 0.55769 0.81118 0.1413 0.10493
Eigen has other a lot of other decompositions, see its documentation for more details.
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