# 7. Eigenvalues and Eigenvectors: Properties

#### Michael Friendly

#### 2025-10-05

## Setup

This vignette uses an example of a \(3 \times 3\) matrix to illustrate some properties of eigenvalues and eigenvectors. We could consider this to be the variance-covariance matrix of three variables, but the main thing is that the matrix is square and symmetric, which guarantees that the eigenvalues, \(\lambda_i\) are real numbers. Covariance matrices are also positive semi-definite, meaning that their eigenvalues are non-negative, \(\lambda_i \ge 0\).

    A <- matrix(c(13, -4, 2, -4, 11, -2, 2, -2, 8), 3, 3, byrow=TRUE)
    A

    ##      [,1] [,2] [,3]
    ## [1,]   13   -4    2
    ## [2,]   -4   11   -2
    ## [3,]    2   -2    8

Get the eigenvalues and eigenvectors using `eigen()`; this returns a named list, with eigenvalues named `values` and eigenvectors named `vectors`.

    ev <- eigen(A)
    # extract components
    (values <- ev$values)

    ## [1] 17  8  7

    (vectors <- ev$vectors)

    ##         [,1]    [,2]   [,3]
    ## [1,]  0.7454  0.6667 0.0000
    ## [2,] -0.5963  0.6667 0.4472
    ## [3,]  0.2981 -0.3333 0.8944

The eigenvalues are always returned in decreasing order, and each column of `vectors` corresponds to the elements in `values`.

## Properties of eigenvalues and eigenvectors

The following steps illustrate the main properties of eigenvalues and eigenvectors. We use the notation \(A = V' \Lambda V\) to express the decomposition of the matrix \(A\), where \(V\) is the matrix of eigenvectors and \(\Lambda = diag(\lambda_1, \lambda_2, \dots, \lambda_p)\) is the diagonal matrix composed of the ordered eigenvalues, \(\lambda_1 \ge \lambda_2 \ge \dots \lambda_p\).

0. Orthogonality: Eigenvectors are always orthogonal, \(V' V = I\). `zapsmall()` is handy for cleaning up tiny values.

    crossprod(vectors)

    ##           [,1]      [,2]      [,3]
    ## [1,] 1.000e+00 3.053e-16 5.551e-17
    ## [2,] 3.053e-16 1.000e+00 0.000e+00
    ## [3,] 5.551e-17 0.000e+00 1.000e+00

    zapsmall(crossprod(vectors))

    ##      [,1] [,2] [,3]
    ## [1,]    1    0    0
    ## [2,]    0    1    0
    ## [3,]    0    0    1

1. trace(A) = sum of eigenvalues, \(\sum \lambda_i\).

    library(matlib)   # use the matlib package
    tr(A)

    ## [1] 32

    sum(values)

    ## [1] 32

2. sum of squares of A = sum of squares of eigenvalues, \(\sum \lambda_i^2\).

    sum(A^2)

    ## [1] 402

    sum(values^2)

    ## [1] 402

3. determinant = product of eigenvalues, \(\det(A) = \prod \lambda_i\). This means that the determinant will be zero if any \(\lambda_i = 0\).

    det(A)

    ## [1] 952

    prod(values)

    ## [1] 952

4. rank = number of non-zero eigenvalues

    R(A)

    ## [1] 3

    sum(values != 0)

    ## [1] 3

5. eigenvalues of the inverse \(A^{-1}\) = 1/eigenvalues of A. The eigenvectors are the same, except for order, because eigenvalues are returned in decreasing order.

    AI <- solve(A)
    AI

    ##          [,1]    [,2]     [,3]
    ## [1,]  0.08824 0.02941 -0.01471
    ## [2,]  0.02941 0.10504  0.01891
    ## [3,] -0.01471 0.01891  0.13340

    eigen(AI)$values

    ## [1] 0.14286 0.12500 0.05882

    eigen(AI)$vectors

    ##        [,1]    [,2]    [,3]
    ## [1,] 0.0000  0.6667  0.7454
    ## [2,] 0.4472  0.6667 -0.5963
    ## [3,] 0.8944 -0.3333  0.2981

6. There are similar relations for other powers of a matrix, \(A^2, \dots A^p\): `values(mpower(A,p)) = values(A)^p`, where `mpower(A,2) = A %*% A`, etc.

    eigen(A %*% A)

    ## eigen() decomposition
    ## $values
    ## [1] 289  64  49
    ##
    ## $vectors
    ##         [,1]    [,2]   [,3]
    ## [1,]  0.7454  0.6667 0.0000
    ## [2,] -0.5963  0.6667 0.4472
    ## [3,]  0.2981 -0.3333 0.8944

    eigen(A %*% A %*% A)$values

    ## [1] 4913  512  343

    eigen(mpower(A, 4))$values

    ## [1] 83521  4096  2401