eigenVc: Compute the eigenvectors of a square symmetric matrix

In psvd: Eigendecomposition, Singular-Values and the Power Method

Compute the eigenvectors of a square symmetric matrix

Description

This is an internal function which uses a C code to calculate an eidendecomposition of a square symmetric matrix. This function is used in the power method allowing to compute singular values and principal component analysis.

Usage

  eigenVc(xmat, wp, d, r, itmax, err)

Arguments

xmat	Square symmetric matrix of order d.
wp	Columns orthogonal matrix of size (d,r), r <= d.
d	Number of rows of wp.
r	Number of columns of wp.
itmax	Maximum number of iterations.
err	Tolerance level in the iterative search.

Value

This function returns a data frame containing 2 components

wc	Eigenvectors matrix.
iter	Number of iterations by the power method.

Examples

d <- 3
r <- 3
w <- c(rnorm(d*r,0,1))
res <- mGSc(w, d, r)
wp <- res$wp
XtX <- c(3,2,1,2,1,0,1,0,1)
res <- eigenVc(XtX, wp, d, r, itmax=100, err=1e-8)
wc <- matrix(res$wc, d, r)
t(wc) 

psvd documentation built on Oct. 25, 2024, 9:07 a.m.