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.

#include <RcppEigen.h>

// [[Rcpp::depends(RcppEigen)]]

using Eigen::Map;               	// 'maps' rather than copies 
using Eigen::MatrixXd;                  // variable size matrix, double precision
using Eigen::VectorXd;                  // variable size vector, double precision
using Eigen::SelfAdjointEigenSolver;    // one of the eigenvalue solvers

// [[Rcpp::export]]
VectorXd getEigenValues(Map<MatrixXd> M) {
    SelfAdjointEigenSolver<MatrixXd> es(M);
    return es.eigenvalues();
}

We can illustrate this easily via a random sample matrix.

set.seed(42)
X <- matrix(rnorm(4*4), 4, 4)
Z <- X %*% t(X)

getEigenValues(Z)


[1]  0.3319  1.6856  2.4099 14.2100

In comparison, R gets the same results (in reverse order) and also returns the eigenvectors.

eigen(Z)


$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.