Lecture 5: Eigenvalues (and eigenvectors)


Week 1: Python intro
Week 2: Matrices, vectors, norms, ranks
Week 3: Linear systems, eigenvectors, eigenvalues

Recap of the previous lecture

  • Linear systems
  • Gaussian elimination
  • Sparse matrices
  • and the condition number!

Today lecture

Today we will talk about:

  • Eigenvectors and their applications (PageRank, MATLAB logo)
  • Gershgorin circles
  • Computing eigenvectors using power method
  • Schur theorem
  • Normal matrices

What is an eigenvector

An vector $x \ne 0$ is called an eigenvector of a square matrix $A$ if there exists a number $\lambda$ such that
$$ Ax = \lambda x. $$ The number $\lambda$ is called an eigenvalue.
The names eigenpair and eigenproblem are also used.

Why eigenvectors and eigenvalues are important

Eigenvectors are both important auxiliary tools and also play important role in applications.
To start with all our microworld is governed by the Schrodinger equation which is an eigenvalue problem.
$$ H \psi = E \psi, $$ where $E$ is the ground state energy, $\psi$ is called wavefunction and $H$ is the Hamiltonian.
More than 50% of the computer power is spent on solving this type of problems for computational material / drug design.

Google PageRank

One of the most famous eigenvectors computation is the Google PageRank. It is not actively used by Google nowdays, but it was of the main features in its early stages. The question is how do we rank webpages, which one is important, and which one is not. All we know about the web is which page referrs to which. PageRank is defined by a recursive definition. Denote by $p_i$ the importance of the $i$-th page. Then we define this importance as an average value of all importances of all pages that refer to the current page. It gives us a linear system
$$ p_i = \sum_{j \in N(i)} \frac{p_j}{L(j)}, $$ where $L(j)$ is the number of outgoing links on the $j$-th page, $N(i)$ are all the neighbours. It can be rewritten as
$$ p = G p, $$ or as an eigenvalue problem

$$ Gp = 1 p, $$

i.e. the eigenvalue $1$ is already known.


We can compute it using some Python packages. We will use networkx package for working with graphs that can be installed using
conda install networkx

We will use a simple example of Zachary karate club network. This data was collected in 1977, and is a classical social network dataset.

In [2]:
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import networkx as nx
kn = nx.read_gml('karate.gml')
nx.draw_networkx(kn) #Draw the graph

Now we can actually compute the PageRank using the NetworkX built-in function. We also plot the size of the nodes larger if its PageRank is larger.

In [3]:
pr = nx.algorithms.link_analysis.pagerank(kn)
pr_vector = [pr[i+1] for i in xrange(len(pr))]
pr_vector = np.array(pr_vector) * 5000
nx.draw_networkx(kn, node_size=pr_vector, labels=None)
plt.title('PageRank nodes')

<matplotlib.text.Text at 0x10fa0c610>

Computation of the eigenvalues via characteristic equations

The eigenvalue problem has the form
$$ Ax = \lambda x, $$ or $$ (A - \lambda I) x = 0, $$ therefore matrix $A - \lambda I$ has non-trivial nullspace and should be singular. That means, that the determinant
$$ p(\lambda) = \det(A - \lambda I) = 0. $$ The equation is is called characteristic equations and is a polynomial of order $n$.
The $n$-degree polynomial has $n$ complex roots!

The characteristic equation can be used to compute the eigenvalues, which leads to naive algorithm:

  1. Compute coefficients of the polynomial
  2. Compute the roots

Is this a good idea?

We can do a short demo of this

In [15]:
import numpy as np
n = 100
a = [[1.0 / (i - j + 0.5) for i in xrange(n)] for j in xrange(n)]
a = np.array(a)
ev = np.linalg.eigvals(a)
#print 'Eigenvalues using Lapack function:', ev

#There is a special numpy function for chacteristic polynomial
cf = np.poly(a)
ev_roots = np.roots(cf)
#print 'Coefficients of the polynomial:', cf
#print 'Polynomial roots:', ev_roots
plt.scatter(ev.real, ev.imag, marker='x', label='Lapack')
b = a + 1e-1 * np.random.randn(n, n)
ev_b = np.linalg.eigvals(b)
plt.scatter(ev_b.real, ev_b.imag, marker='o')
#plt.scatter(ev_roots.real, ev_roots.imag, marker='o', label='Brute force')
plt.xlabel('Real part')
plt.ylabel('Imaginary part')

<matplotlib.text.Text at 0x1114b70d0>


  • Do not do that, unless you have a reason.
  • Polynomial rootfinding is very ill-conditioned (can be much better, but not with monomials!)

Gershgorin circles

There is a very interesting theorem that often helps to localize the eigenvalues. It is called Gershgorin theorem.
It states that all the eigenvalues $\lambda_i, \quad i = 1, \ldots, n$ are located inside the union of Gershgorin circles $C_i$, where $C_i$ is a disk on the complex plane with center $a_{ii}$ and radius
$$r_i = \sum_{j \ne i} |a_{ij}|.$$ Moreover, if the circles do not intersect, that contain only one eigenvalues. The proof is instructive, since it uses the concepts we looked at the previous lectures.


First we need to show, that if the matrix $A$ is strictly diagonally dominant, i.e. $$ |a_{ii}| > \sum_{j \ne i} |a_{ij}|, $$ then such matrix is non-singular.

We separate the diagonal part and off-diagonal part, and get
$$ A = D + S, $$ and $\Vert D^{-1} S\Vert_1 < 1$. Therefore, by using the Neumann series, the matrix $D + S$ is invertible.

Now the proof follows by contradiction: if any of the eigenvalues lies outside all of the circles, the matrix $(A - \lambda I)$ is strictly diagonally dominant, and thus is invertible. That means, that $(A - \lambda I) x = 0$ means $x = 0$.

A short demo

In [20]:
import numpy as np
%matplotlib inline
n = 30
fig, ax = plt.subplots(1, 1)
a = [[1.0 / (i - j + 0.5) for i in xrange(n)] for j in xrange(n)]
#a = np.array(a)
#a = np.diag(np.arange(n))
#a = a + 0.1*np.random.randn(n, n)
u = np.random.randn(n, n)
a = np.linalg.inv(u).dot(a).dot(u)
xg = np.diag(a).real
yg = np.diag(a).imag
rg = np.zeros(n)
ev = np.linalg.eigvals(a)
for i in xrange(n):
    rg[i] = np.sum(np.abs(a[i, :])) - np.abs(a[i, i])
    crc = plt.Circle((xg[i], yg[i]), radius=rg[i], fill=False)
plt.scatter(ev.real, ev.imag, label='x', color='r')
ax.set_title('Eigenvalues and Gershgorin circles')

Note: There are more complicated figures, like Cassini ovals, that include the spectra.

Power method

We are often interested in the computation of the part of the spectrum, like the largest eigenvalues, smallest eigenvalues. Also it is interesting to note that for the Hermitian matrices $(A = A^*)$ the eigenvalues are always real (prove it!).

A power method is the simplest method for the computation of the largest eigenvalue in modulus. It is also our first example of the iterative method and Krylov method.

Power method has the form $$ x_{k+1} = A x_k, \quad x_{k+1} := \frac{x_{k+1}}{\Vert x_{k+1} \Vert}. $$ The convergence is geometric, but the convergence ratio is $q^k$, where $q = |\frac{\lambda_{2}}{\lambda_{1}}| \leq 1$ and $k$ is the number of iterations. It means, the convergence can be artitrary small. To prove it, it is sufficient to consider a $2 \times 2$ diagonal matrix.

Things to remember about the power method

  • One step requires one matrix-by-vector product. If the matrix allows for an $\mathcal{O}(n)$ matvec (for example, it is sparse), then it is possible.
  • Convergence can be slow
  • If only a rough estimate is needed, only a few iterations are needed
  • The solution vector is in the Krylov subspace and has the form $\mu A^k x_0$, where $\mu$ is a normalization constant.

First matrix decomposition: the Schur form

There is one class of matrices when eigenvalues can be found easily: triangular matrices

$$ A = \begin{pmatrix} \lambda_1 & * & * \\ 0 & \lambda_2 & * \\ 0 & 0 & \lambda_3 \\ \end{pmatrix}. $$

The eigenvalues of $A$ are $\lambda_1, \lambda_2, \lambda_3$. Why?

Because the determinant is
$$ \det(A - \lambda I) = (\lambda - \lambda_1) (\lambda - \lambda_2) (\lambda - \lambda_3). $$

Thus, computing the eigenvalues of triangular matrices is easy. Now, the unitary matrices come to help. Let $U$ be a unitary matrix, i.e. $U^* U = I$. Then $$ \det(A - \lambda I) = \det(U (U^* A U - \lambda I) U^*) = \det(UU^*) \det(U^* A U - \lambda I) = \det(U^* A U - \lambda I), $$ where we have used the famous multiplicativity property of the determinant, $\det(AB) = \det(A) \det(B)$. It means,
that the matrices $U^* A U$ and $A$ have the same characteristic polynomials, and the same eigenvalues.

If we manage to make $U^* A U = T$, where $T$ is upper triangular then we are done. Multplying from the left and the right by $U$ and $U^*$ respectively, we get the desired decomposition:
$$ A = U T U^*. $$ This is the celebrated Schur decomposition. Recall that unitary matrices mean stability, thus the eigenvalues are computed very accuratedly. The Schur decomposition shows why we need matrix decompositions: it represents a matrix into a product of three matrices with structure.

Schur theorem
Every $n \times n$ matrix can be represented in the Schur form.
Sketch of the proof.

  1. Every matrix has at least $1$ non-zero eigenvector (take a root of characteristic polynomial, $(A-\lambda I)$ is singular, has non-trivial nullspace). Let $Ax_1 = \lambda_1 x_1, \quad \Vert x_1 \Vert_2 = 1$
  2. We find a Householder matrix $U_1$ such that $U_1 x_1 = e_1$. Then, $$ U^*_1 A U_1 = \begin{pmatrix} 1 & * \\ 0 & A_2 \end{pmatrix}, $$ where $A_2$ is an $(n-1) \times (n-1)$ matrix. This is called block triangular form. We can now work with $A_2$ only and so on.
    Note: This is not an algorithm, but a proof that Schur form exists.

Application of the Schur theorem

Important application of the Schur theorem: Normal matrices.
Definition. A matrix is called a normal matrix, if
$$ AA^* = A^* A. $$ Example: Hermitian matrices, unitary matrices.

Theorem: $A$ is a normal matrix, iff $A = U \Lambda U^*$.
Sketch of the proof: One way is obvious (if the decomposition holds, the matrix is normal). The other is more complicated. Consider the Schur form of the matrix $A$. Then $AA^* = A^*A$ means $TT^* = T^* T$. By looking at the elements we immediately see, that the only uppertriangular matrix $T$ that satisfies $TT^* = T^* T$ is a diagonal matrix!

Summary of todays lecture

  • Eigenvalues, eigenvectors
  • Gershgorin theorem
  • Power method
  • Schur theorem
  • Normal matrices

Tomorrow: we will start matrix decompositions!


In [137]:
from IPython.core.display import HTML
def css_styling():
    styles = open("./styles/custom.css", "r").read()
    return HTML(styles)