# Using the GSL to compute eigenvalues

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Two posts showed how to compute eigenvalues using Armadillo and using Eigen. As we also looked at using the

GNU GSL, this post will show how to conpute eigenvalues using GSL.

As mentioned in the previous GSL post, we instantiate C language pointers suitable for GSL (here the matrix `M`

). Those *must* be freed manually, as shown before the `return`

statement.

// [[Rcpp::depends(RcppGSL)]] #include#include #include // [[Rcpp::export]] Rcpp::NumericVector getEigenValues(Rcpp::NumericMatrix sM) { RcppGSL::matrix M(sM); // create gsl data structures from SEXP int k = M.ncol(); RcppGSL::vector ev(k); // instead of gsl_vector_alloc(k); gsl_eigen_symm_workspace *w = gsl_eigen_symm_alloc(k); gsl_eigen_symm (M, ev, w); gsl_eigen_symm_free (w); // we need to invoke wrap() here to help the compiler, but at least we save a loop // we also need to create a new Rcpp object as the RcppGSL one needs to be free'd. Rcpp::NumericVector res(Rcpp::wrap(ev)); M.free(); // important: GSL wrappers use C structure ev.free(); return res; // return results vector }

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] 14.2100 2.4099 1.6856 0.3319

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

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