This function computes the first \(D\) eigenvalues and eigenvectors of a
symmetric positive definite matrices. The eigenvalues are computed
by the Rayleigh quotient method (Lange, 2010, p. 120).
Usage
sirt_eigenvalues( X, D, maxit=200, conv=10^(-6) )
Arguments
- X
Symmetric matrix
- D
Number of eigenvalues to be estimated
- maxit
Maximum number of iterations
- conv
Convergence criterion
Value
A list with following entries:
- d
Vector of eigenvalues
- u
Matrix with eigenvectors in columns
References
Lange, K. (2010). Numerical Analysis for Statisticians.
New York: Springer.
Examples
Sigma <- diag(1,3)
Sigma[ lower.tri(Sigma) ] <- Sigma[ upper.tri(Sigma) ] <- c(.4,.6,.8 )
sirt::sirt_eigenvalues(X=Sigma, D=2 )
# compare with svd function
svd(Sigma)