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Eigenvalues and Eigenvectors Algorithms

We now discuss the numerical algorithms for computing eigenvalues and eigenvectors. We will mainly focus on the symmetric matrices with real eigenvalues and eigenvectors. For the general case, most of the tasks in data science need to conduct the singular value decomposition (SVD).

Recall the symmetric matrix \(A\) has the eigenvalue decomposition \(A = U\Lambda U^T\), where \(U = (u_1, u_2, \cdots, u_n)\) is an orthogonal matrix with the columns \(u_1, u_2, \cdots, u_n\) being the eigenvectors of \(A\) and \(\Lambda = \text{diag}(\lambda_1, \lambda_2, \cdots, \lambda_n)\) is a diagonal matrix with diagonal entries \(\lambda_1\ge \lambda_2\ge \cdots \ge \lambda_n\) being the eigenvalues of \(A\).

Sensitivity of Eigenvalues and Eigenvectors

There are two famous theorems about the sensitivity of eigenvalues and eigenvectors.

Theorem(Weyl's inequality): If \(A, B \in \mathbb{R}^{n \times n}\) are two symmetric matrices, then for any eigenvalue \(\lambda_j\) of \(A\) and \(\mu_j\) of \(B\), we have:

\[ |\lambda_j - \mu_j| \le \|A - B\|_2. \]

Theorem(Davis-Kahan Theorem): If \(A, B \in \mathbb{R}^{n \times n}\) are two symmetric matrices, with eigenvalues \(\lambda_1 \ge \lambda_2 \ge \cdots \ge \lambda_n\) and \(\mu_1 \ge \mu_2 \ge \cdots \ge \mu_n\), let \(u_j, v_j\) be the \(j\)-th eigenvector of \(A\) and \(B\) corresponding to \(\lambda_j\) and \(\mu_j\), we have:

\[ \|u_j - v_j\| \le \frac{2^{1.5}\|A - B\|}{\min(\lambda_{j-1} - \lambda_j, \lambda_{j} - \lambda_{j+1})}. \]

From the above two theorems, we can see that the eigenvalues are stable but the eigenvectors will be sensitive when the eigengap \(\Delta_j = \min(\lambda_{j-1} - \lambda_j, \lambda_{j} - \lambda_{j+1})\) is small.

Be aware that the above theorems are the simplified version. We can refer to the Matrix Perturbation Theory for more details.

Power Method

Here we consider the positive definite matrix \(A\) with the eigenvalues \(\lambda_1 > \lambda_2 \ge \cdots \ge \lambda_n >0\).

The power method applies the power of the matrix \(A\):

\[ A^k = U \Lambda^k U^T = \sum_{j=1}^n \lambda_j^k u_j u_j^T. \]

Let us take a vector \(x_0 \in \mathbb{R}^n\) which can be expressed as:

\[ x_0 = \alpha_1 u_1 + \alpha_2 u_2 + \cdots + \alpha_n u_n, \]

where \(\alpha_i \in \mathbb{R}\). Thus, we have:

\[ \begin{aligned} A^k x_0 &= \sum_{j=1}^n \alpha_j A^k u_j \\ &= \lambda_1^k \left[ \alpha_1 u_1 + \sum_{j=2}^n \alpha_j \left( \frac{\lambda_j}{\lambda_1} \right)^k u_j \right]. \end{aligned} \]

Therefore, it follows that:

\[ \lim_{k \to \infty} \frac{A^k x_0}{\lambda_1^k} = \alpha_1 u_1. \]

This indicates that, when \(\alpha_1 \neq 0\) and \(k\) is sufficiently large, the vector:

\[ x_k = \frac{A^k x_0}{\lambda_1^k} \]

is a very good approximation of an eigenvector of \(A\).

This motivates the power method to find the largest eigenvalue and its corresponding eigenvector.

\[ \begin{aligned} x_{k+1} &= A x_k \\ x_{k+1} &= \frac{x_{k+1}}{||x_{k+1}||} \end{aligned} \]

Notice that we normalize the vector \(x_{k+1}\) in each iteration as the eigenvector is only determined by the direction.

import numpy as np
A = np.array([[4, 1],
              [2, 3]])
tol = 1e-6
max_iter = 1000
# Initialize a random vector
n = A.shape[0]
x = np.random.rand(n)

for _ in range(max_iter):
    # Multiply A with the vector
    x = A @ x
    x_norm = np.linalg.norm(x)    
    # Normalize the vector
    x = x / x_norm
    # Check for convergence
    if np.linalg.norm(x_new - x) < tol:
        break

Convergence and Sensitivity: We can see from the above analysis that the power method convergence speed is determined by \(\lambda_2/\lambda_1\). If \(\lambda_2\) is close to \(\lambda_1\), the convergence is slow. This also tells us that the convergence speed of the power method is determined by eigengap.

Other eigenvectors: The power method above only finds the largest eigenvalue and its corresponding eigenvector. As \(A^{-1}\) has the largest eigenvalues \(\lambda_n^{-1}\) with the eigenvector \(u_n\), we can use the power method to \(A^{-1}\) to get the smallest eigenvalue and its corresponding eigenvector. This is called the inverse power method.

\[ \begin{aligned} x_{k+1} &= A^{-1} x_k \\ x_{k+1} &= \frac{x_{k+1}}{||x_{k+1}||} \end{aligned} \]

We can see that the inverse power method needs to solve a linear system. You can improve the efficiency by apply the LU decomposition first. Similar to the above analysis, the convergence speed of the inverse power method is determined by \(\lambda_n/\lambda_{n-1}\).

We can further apply the inverse power method to \(A - \mu I\) with some shift \(\mu\) to accelerate the convergence. This is called the shifted inverse power method. Without loss of generality, if we want to compute \(\lambda_1\), and we assume \(0<|\lambda_1 - \mu| < |\lambda_2 - \mu| \le \cdots \le |\lambda_n - \mu|\). The convergence speed is determined by \(|\lambda_k - \mu|/|\lambda_{k+1} - \mu|\), the shift \(\mu\) should be chosen as close to \(\lambda_k\) as possible to accelerate the convergence. You may worry that when \(\mu\) is too close to \(\lambda_k\), linear equation for \(A-\mu I\) is ill-conditioned. Actually, it can be shown that the illness of \(A-\mu I\) will not affect the convergence of the inverse power method.

QR Method

Though the power method is simple and easy to implement, it depends on the eigenvalue distribution. Besides, we can only find one eigenvalue and its corresponding eigenvector using the power method. We now discuss how to get the full eigen-decomposition using the QR method.

QR Decomposition: For any matrix \(A\), there exists a QR decomposition \(A = QR\), where \(Q\) is an orthogonal matrix and \(R\) is an upper triangular matrix. QR decomposition can be computed by the Householder transformation with the complexity \(O(n^3)\).

QR decomposition can be applied to any matrix \(A\), but the \(Q, R\) could be complex if \(A\) is not symmetric. So we mainly focus on the symmetric matrices in the following discussion. Also, for rectangular matrix \(A \in \mathbb{R}^{m \times n}\) with \(m > n\), we can also apply the QR decomposition as

\[ A = Q \begin{pmatrix} R \\ 0 \end{pmatrix} \]

where \(R\) is an upper triangular matrix.

QR Iteration: We can use the QR iteration to find the eigenvalues and eigenvectors: Given \(A_0 = A\),

\[ \begin{aligned} A_{k} &= Q_k R_k \\ A_{k+1} &= R_k Q_k \end{aligned} \]

Theorem(Convergence of QR Iteration): If \(A\) has distinct eigenvalues \(|\lambda_1| > |\lambda_2| > \cdots > |\lambda_n|>0\), then \(A_k\) will converge to an upper triangular matrix with the eigenvalues on the diagonal. \(\tilde Q_k = Q_1 Q_2 \cdots Q_k\) will converge to the eigenvectors.

QR Iteration

In fact, we can see from the QR iteration that

\[ A^k = Q_1 Q_2 \cdots Q_k R_k R_{k-1} \cdots R_1. \]

Therefore, the QR algorithm can be seen as a more sophisticated variation of the basic "power" eigenvalue algorithm.

Here's a simple implementation of the QR iteration method:

import numpy as np

# Create a symmetric test matrix
n = 4
A = np.random.rand(n, n)
A = (A + A.T) / 2  

# Initialize
A_k = A.copy()
V = np.eye(n)  # To accumulate eigenvectors
tol = 1e-8
max_iter = 1000

# QR iteration
for _ in range(max_iter):
    # QR decomposition
    Q, R = np.linalg.qr(A_k)

    # Update A_k
    A_k = R @ Q

    # Accumulate eigenvectors
    V = V @ Q

    # Check convergence of off-diagonal elements
    if np.sum(np.abs(A_k - np.diag(np.diag(A_k)))) < tol:
        break

# Eigenvalues are on the diagonal of final A_k
eigenvals = np.diag(A_k)

Practical Algorithms

We will briefly discuss the practical algorithms for computing eigenvalues and eigenvectors below. Please refer to the specialized textbooks, e.g., Matrix Computations, for more details.

The QR iteration method above is just the basic version. It can be further accelerated by applying the shift technique like the power method. In fact, the state-of-the-art Francis algorithm applies the famous implicit double-shift with no QR decomposition being explicitly performed. In fact, instead of QR decomposition, the Francis algorithm decomposes matrix into \(QH\) form, where \(Q\) is orthogonal but \(H\) is an upper Hessenberg matrix. The complexity of the Francis algorithm is \(O(n^3)\).

When \(A\) is symmetric, we ususally apply the tridiagonalization to \(A = QTQ^T\) first, where \(T\) is a tridiagonal matrix. Then we can apply an implicit QR iteration to \(T\) with the Wilkinson shift. This is one of the most remarkable algorithms in the numerical linear algebra. We refer to the notes for more details.

\[ \begin{array}{ccc} \text{Upper Triangular} & \text{Upper Hessenberg} & \text{Tridiagonal} \\ \begin{bmatrix} \times & \times & \times & \times & \times \\ 0 & \times & \times & \times & \times \\ 0 & 0 & \times & \times & \times \\ 0 & 0 & 0 & \times & \times \\ 0 & 0 & 0 & 0 & \times \end{bmatrix} & \begin{bmatrix} \times & \times & \times & \times & \times \\ \times & \times & \times & \times & \times \\ 0 & \times & \times & \times & \times \\ 0 & 0 & \times & \times & \times \\ 0 & 0 & 0 & \times & \times \end{bmatrix} & \begin{bmatrix} \times & \times & 0 & 0 & 0 \\ \times & \times & \times & 0 & 0 \\ 0 & \times & \times & \times & 0 \\ 0 & 0 & \times & \times & \times \\ 0 & 0 & 0 & \times & \times \end{bmatrix} \end{array} \]

For SVD decomposition \(A = U\Sigma V^T \in \mathbb{R}^{m \times n}\), \(m \ge n\), as \(U\) is the eigenvector matrix of \(A^TA\) and \(V\) is the eigenvector matrix of \(A^TA\), we can apply the QR iteration to \(A^TA\) to get the eigenvalues and eigenvectors of \(A^TA\). However, the practical SVD algorithm will not conduct eigen-decomposition of \(A^TA\) and \(A^TA\) explicitly. Refer to the Golub-Reinsch algorithm for more details. The complexity of the Golub-Reinsch algorithm is usually \(O(m^2n+n^2m + n^3)\).

Python Implementation

Like the linear equation, both numpy and scipy provide the linalg module for the eigenvalue and eigenvector problems.

You may need to consider to use different functions based on:

  • Whether \(A\) is symmetric
  • Whether you only need the eigen/singular values
  • Whether you only need the top \(k\) or the complete decomposition
import numpy as np

A = np.array([[1, 2],
              [2, 3]])

# Compute eigenvalues and eigenvectors
eigenvalues, eigenvectors = np.linalg.eig(A)
# If you only need eigenvalues
eigenvalues = np.linalg.eigvals(A)
# Eigendecomposition for symmetric matrices
eigenvalues, eigenvectors = np.linalg.eigh(A)
# If you only need eigenvalues
eigenvalues = np.linalg.eigvalsh(A)
# Perform SVD
U, S, VT = np.linalg.svd(A, full_matrices=False) #If full_matrices = True (default), U and VT are of shape (M, M), (N, N). If False, the shapes are (M, K) and (K, N), where K = min(M, N).
# If you only need singular values
singular_values = np.linalg.svdvals(A)

# Compute the largest k eigenvalues and eigenvectors, when k << n
from scipy.sparse.linalg import eigs, eigsh, svds
k = 1
eigenvalues, eigenvectors = eigs(A, k=k, which='LM')  # 'LM' selects largest magnitude eigenvalues
# For symmetric matrices
eigenvalues, eigenvectors = eigsh(A, k=k, which='LA')  # 'LA' selects largest algebraic eigenvalues
# Top k singular values and vectors
U, S, VT = svds(A, k=k)

The scipy.sparse.linalg package provides more efficient implementations for large sparse matrices and it implements the specialized algorithms if you only need top \(k\) eigenvalues and eigenvectors. Refer to the scipy documentation for more details. You only need to consider to use the scipy.sparse.linalg for the top \(k\) eigenvalues and eigenvectors when \(k \ll n\) and \(n\) is super large. We encourage you to test the computational efficiency for the above code to see the difference using different methods.