STAT 207: Vectors and matrices¶

Zhe Fei (zhe.fei@ucr.edu)¶

  • NAS Chapter 6

Many scientific computational problems involve vectors and matrices.

The distinction between the set of real numbers, $\mathbb{R}$, and the set of floating-point numbers, $\mathbb{F}$, that we use in the computer has important implications for numerical computations.

We will consider the floating-point representation of elements of vectors and matrices and the computations in $\mathbb{F}$.

In multidimensional calculus, vector and matrix norms quantify notions of topology and convergence.

  • Norms are devices for deriving explicit bounds, theoretical developments in numerical analysis rely heavily on norms.

  • They are particularly useful in establishing convergence and in estimating rates of convergence of iterative methods for solving linear and nonlinear equations.

  • Norms also arise in almost every other branch of theoretical numerical analysis. Functional analysis, which deals with infinite-dimensional vector spaces, uses norms on functions.

Elementary Properties of Vector Norms¶

Euclidean vector norm: $$ \| x\|_2 = \sqrt{\sum_{i=1}^m x_i^2 } $$

A norm on $R^m$ is formally defined by four properties:

(a) $\| x\| \ge 0$,

(b) $\| x\| = 0$ if and only if $x = \mathbf{0}$,

(c) $\| cx\| = |c|\cdot \|x\|$ for all real number $c$,

(d) $\| x+y\| \le \|x\| + \|y\|$.

$\ell$-1 norm: $$ \| x\|_1 = \sum_{i=1}^m |x_i|, $$

$\ell$-$\infty$ norm: $$ \| x\|_\infty = \max_{i} |x_i|. $$

For each of the norms $\| x\|_p, p = 1, 2$, and $\infty$, a sequence of vectors $x_n$ converges to a vector $y$ if and only if each component sequence $x_{ni}$ converges to $y_i$. Thus, all three norms give the same topology on $R^m$.

NAS Proposition 6.2.1 Let $\| x\|$ be any norm on $R^m$. Then there exist positive constants $k_l$ and $k_u$ such that $k_l\| x\|_1 \le \| x\|\le k_u\| x\|_1$ holds for all $x \in R^m$.

  • $\sup_{x\ne 0} \| x\|/\| x\|^*$ is finite for any pair of norms $\| x\|$ and $\| x\|^*$.

  • For $p<q$ from $\{1,2,\infty\}$,

$$ \| x\|_q \le \| x\|_p $$

$$ \| x\|_p \le m^{1/p - 1/q} \| x\|_q $$

Elementary Properties of Matrix Norms¶

An $m\times m$ matrix $A = (a_{ij})$ is simply a vector in $R^{m^2}$.

It is preferred that a matrix norm is compatible with matrix multiplication.

In addition to (a) - (d), we require

(e) $\|AB\| \le \|A\| \cdot \|B\|$

for any product of $m\times m$ matrices $A$ and $B$.

  • Frobenius norm: $\|A\|_F = \sqrt{\sum_{i=1}^m \sum_{j=1}^m a_{ij}^2 } = \sqrt{tr(AA^T) } = \sqrt{tr(A^TA) }$

  • Matrix norm induced from any vector norm $\|x\|$ on $R^{m}$: $$ \|A\| = \sup_{x\ne 0} \frac{\|Ax\|}{\|x\|} = \sup_{\|x\| = 1} \|Ax\|. $$

  • What is $\|I\|$ and $\|I\|_F$?

In infinite-dimensional settings, induced matrix norms are called operator norms.

NAS Proposition 6.2.1 If $A = (a_{ij})$ is an $m \times m$ matrix, then

(a) $\|A\|_1 = \max_j \sum_i |a_{ij}|$,

(b) $\|A\|_2 = \sqrt{\rho(A^TA)}$, which reduces to $\rho(A)$ if $A$ is symmetric,

(c) $\|A\|_2 = \max_{\|u\|_2=1, \|v\|_2=1} u^TAv$,

(d) $\|A\|_\infty = \max_i\sum_j|a_{ij}|$.

Norm Preserving Linear Transformations¶

  • Orthogonal matrix: $OO^T = I$;

  • Orthonormal set of vectors $S$: every vector in S has magnitude 1 and the set of vectors are mutually orthogonal.

Following $(\det O)^2 = 1$, we can divide the orthogonal matrices into rotations with $\det O = 1$ and reflections with $\det O = -1$. Take $2 \times 2$ matrices for example,

  • Rotation of angle $\theta$ can be presented as: $$ O = \begin{pmatrix} \cos \theta & -\sin \theta \\ \sin \theta & \cos \theta \\ \end{pmatrix} $$

  • Reflection of a point across the line at angle $\theta/2$ with the $x$ axis: $$ O = \begin{pmatrix} \cos \theta & \sin \theta \\ \sin \theta & -\cos \theta \\ \end{pmatrix} $$

Remarks

  • The set of orthogonal matrices forms a group under matrix multiplication.

  • The identity matrix is the unit of the group.

  • The rotations constitute a subgroup of the orthogonal group, but the reflections do not since the product of two reflections is a rotation.

Orthogonal transformations preserve inner products and Euclidean norms: $(Ou)^TOv = u^TO^TOv = u^Tv$.

As a result, all eigenvalues of $O$ lie on the boundary of the unit circle.

Norm invariance for vectors also leads to norm invariance for matrices.

  • $\|OA\|_2^2 = \|A\|_2^2$

  • $\|AO\|_2^2 = \|A\|_2^2$

  • $\|OA\|_F^2 = \|A\|_F^2$

  • $\|AO\|_F^2 = \|A\|_F^2$

Householder matrices¶

Given a unit vector $u$, the Householder matrix $H = I−2uu^T$ represents a reflection across the plane perpendicular to $u$.

$H$ is orthogonal and symmetric, $$ HH^T = I - 4uu^T + 4u\|u\|_2^2 u^T = I. $$

  • $Hv = v$ whenever $v$ lies in the plane perpendicular to $u$.

  • $Hu = -u$.

  • $H$ has one eigenvalue equal to $-1$ and all others equal to 1.

Applications:

  • Matrix decomposition: QR, SVD,
  • Orthogonalization:
  • Stabilizing Numerical Algorithms:

Iterative Solution of Linear Equations¶

Many numerical problems involve iterative schemes of the form \begin{equation}\label{ite_lin}\tag{1} x_n = Bx_{n-1} + w \end{equation} for solving the vector-matrix equation $(I-B)x = w$. The map $f(x) = Bx + w$ satisfies $$ \| f(y) - f(x)\| = \|B (y-x) \| \le \|B\|\cdot \|y - x\| $$ and is contractive for a vector norm $\|x\|$ if $\|B\|<1$.

NAS Proposition 6.5.1 Let $B$ be an arbitrary matrix with spectral radius $\rho(B)$ (largest absolute value of the eigenvalues). Then $\rho(B) < 1$ if and only if $\| B \| < 1$ for some induced matrix norm. The inequality $\| B \| < 1$ implies:

(a) $\lim_{n\rightarrow\infty} \left| B^n \right| = 0$,

(b) $\left( I - B \right)^{-1} = \sum_{n=0}^{\infty} B^n$,

(c) $\frac{1}{1+\| B \|}\le \| \left( I - B \right)^{-1} \| \le \frac{1}{1-\| B \|}$.

Linear iteration is especially useful in solving the equation $Ax = b$ for $x$ when an approximation $C$ to $A^{−1}$ is known, let $B = I-CA$ and $w = Cb$ and iterate equation \eqref{ite_lin}.

Jacobi’s Iteration¶

Suppose $A = (a_{ij})$ is strictly diagonally dominant ($|a_{ii}|>\sum_{j\ne i} |a_{ij}|$ ), let $D = \text{diag}(a_{ii})$ be the diagonal matrix. Then $C = D^{-1}$ is an approximate inverse of $A$, and $B = I-CA$ satisfies $\|B\|_\infty <1$.

In [1]:
import numpy as np

def jacobi(A, b, x0, tol=1e-6, max_iter=1000):
    """
    Solves the system of linear equations Ax = b using Jacobi's method.
    """
    n = len(A)
    C = np.diag(1/np.diag(A))
    B = np.identity(n) - C@A
    w = C@b
    
    x = B@x0 + w
    k = 1
    while max(abs(x-x0))> tol:
        x0 = x
        x = B@x0 + w
        k += 1
    
    if k<max_iter:
        print(f'Converged in {k} iterations.')
        return x
    else:
        print(f'Maximum iterations reached.')
        return x
In [2]:
# Example usage
A = np.array([[4, 1, 1], [2, 5, 1], [1, 2, 4]])
b = np.array([4, 1, 2])
x0 = np.array([0, 0, 0])

x = jacobi(A, b, x0)
print(f'Solution: {x}')

print(A@x)

Converged in 28 iterations.
Solution: [ 0.96874976 -0.26562527  0.39062468]
[3.99999845 0.99999787 1.99999795]

Landweber’s Iteration Scheme¶

In practice, the approximate inverse $C$ can be rather crude. $C = \epsilon A^T$ for $\epsilon$ small and positive.

  • $A^TA$ is positive definite with eigenvalues $0< \lambda_1\le ...\le \lambda_m$.

  • $I - \epsilon A^TA$ has eigenvalues $1-\epsilon \lambda_1, ... 1-\epsilon \lambda_m$.

  • Need $1-\epsilon \lambda_m > -1$, so that $\|I - \epsilon A^TA\|_2 < 1$.

Equilibrium Distribution of a Markov Chain¶

Movement among the $m$ states of a Markov chain is governed by its $m\times m$ transition matrix $P = (p_{ij})$, whose entries are nonnegative and satisfy $\sum_j p_{ij} = 1$ for all $i$.

A column vector $x$ with nonnegative entries and norm $\|x\|_1 = \sum_i x_i = 1$ is said to be an equilibrium (stationary) distribution for $P$ provided $x^TP = x^T$, or equivalently $Qx = x$ for $Q = P^T$.

Let $S = \{x: x_i\ge 0, i=1,2,..m, \sum_i x_i =1 \}$. Assume for some power $k$, $Q^k$ has all positive entries, then consider the matrix $R = Q^k - c\mathbf{1}\mathbf{1}^T$.

  • The map $x \rightarrow Q^k x$ is contractive on $S$ with unique fixed point $x_\infty$.

  • $Qx_\infty = x_\infty$.

  • $\lim_{n\rightarrow \infty} Q^n x = x_\infty$ for all $x\in S$.

This power method is also used in PageRank:

  • Eld$\acute{e}$n L (2007) Matrix Methods in Data Mining and Pattern Recognition. SIAM, Philadelphia

  • Langville AN, Meyer CD (2006) Google’s PageRank and Beyond: The Science of Search Engine Rankings. Princeton University Press, Princeton NJ

Condition Number of a Matrix¶

Consider the matrix $$ A = \begin{pmatrix} 10 & 7& 8& 7 \\ 7& 5& 6& 5 \\ 8& 6& 10& 9\\ 7& 5& 9& 10 \end{pmatrix} $$ that is symmetric and positive definite, and $Ax = b$.

  • for $b = (32,23,33,31)^T$, we find $x = (1,1,1,1)^T$,

  • for $b+\Delta b=(32.1, 22.9, 33.1, 30.9)^T$, the solution is violently perturbed $x+\Delta x = (9.2,−12.6, 4.5,−1.1)^T$,

  • for $b = (4,3,3,1)^T$, $x = (1,-1,1,-1)^T$,

  • if we perturb $A$ to $A + 0.01I$, the solution is $x+\Delta x = (.59,−.32, .82,−.89)^T$.

How to explain these patterns? Define the condition number $$ \text{cond}(A) = \|A\|\cdot\|A^{-1} \| $$ of matrix $A$ relative to the given norm. We can have

  • $$ \frac{\|\Delta x\|}{\|x\|}\le \text{cond}(A) \frac{\|\Delta b\|}{\|b\|}, $$ if $Ax=b$ and $A(x+\Delta x) = b + \Delta b$.

  • $$ \frac{\|\Delta x\|}{\|x + \Delta x\|}\le \text{cond}(A) \frac{\|\Delta A\|}{\|A\|}, $$ if $Ax=b$ and $(A + \Delta A)(x+\Delta x) = b$.

The condition number $\text{cond}_2(A)$ relative to the matrix norm $\|A\|_2$ is the ratio of the largest and smallest eigenvalues of $A$, which are $\lambda_1 = 0.01015, \lambda_4 = 30.2887$. Therefore, $\text{cond}_2(A) = 2984$.

In [ ]: