# What Is a Schur Decomposition?

A Schur decomposition of a matrix $A\in\mathbb{C}^{n\times n}$ is a factorization $A = QTQ^*$, where $Q$ is unitary and $T$ is upper triangular. The diagonal entries of $T$ are the eigenvalues of $A$, and they can be made to appear in any order by choosing $Q$ appropriately. The columns of $Q$ are called Schur vectors.

A subspace $\mathcal{X}$ of $\mathbb{C}^{n\times n}$ is an invariant subspace of $A$ if $Ax\in\mathcal{X}$ for all $x\in\mathcal{X}$. If we partition $Q$ and $T$ conformably we can write

$\notag A [Q_1,~Q_2] = [Q_1,~Q_2] \begin{bmatrix} T_{11} & T_{12} \\ 0 & T_{22} \\ \end{bmatrix},$

which gives $A Q_1 = Q_1 T_{11}$, showing that the columns of $Q_1$ span an invariant subspace of $A$. Furthermore, $Q_1^*AQ_1 = T_{11}$. The first column of $Q$ is an eigenvector of $A$ corresponding to the eigenvalue $\lambda_1 = t_{11}$, but the other columns are not eigenvectors, in general. Eigenvectors can be computed by solving upper triangular systems involving $T - \lambda I$, where $\lambda$ is an eigenvalue.

Write $T = D+N$, where $D = \mathrm{diag}(\lambda_i)$ and $N$ is strictly upper triangular. Taking Frobenius norms gives $\|A\|_F^2 = \|D\|_F^2 + \|N\|_F^2$, or

$\notag \|N\|_F^2 = \|A\|_F^2 - \displaystyle\sum_{i=1}^n |\lambda_i|^2.$

Hence $\|N\|_F$ is independent of the particular Schur decomposition and it provides a measure of the departure from normality. The matrix $A$ is normal (that is, $A^*A = AA^*$) if and only if $N = 0$. So a normal matrix is unitarily diagonalizable: $A = QDQ^*$.

An important application of the Schur decomposition is to compute matrix functions. The relation $f(A) = Qf(T)Q^*$ shows that computing $f(A)$ reduces to computing a function of a triangular matrix. Matrix functions illustrate what Van Loan (1975) describes as “one of the most basic tenets of numerical algebra”, namely “anything that the Jordan decomposition can do, the Schur decomposition can do better!”. Indeed the Jordan canonical form is built on a possibly ill conditioned similarity transformation while the Schur decomposition employs a perfectly conditioned unitary similarity, and the full upper triangular factor of the Schur form can do most of what the Jordan form’s bidiagonal factor can do.

An upper quasi-triangular matrix is a block upper triangular matrix

$\notag R = \begin{bmatrix} R_{11} & R_{12} & \dots & R_{1m}\\ & R_{22} & \dots & R_{2m}\\ & & \ddots& \vdots\\ & & & R_{mm} \end{bmatrix}$

whose diagonal blocks $R_{ii}$ are either $1\times1$ or $2\times2$. A real matrix $A\in\mathbb{R}^{n \times n}$ has a real Schur decomposition $A = QRQ^T$ in which in which all the factors are real, $Q$ is orthogonal, and $R$ is upper quasi-triangular with any $2\times2$ diagonal blocks having complex conjugate eigenvalues. If $A$ is normal then the $2\times 2$ blocks $R_{ii}$ have the form

$R_{ii} = \left[\begin{array}{@{}rr@{\mskip2mu}} a & b \\ -b & a \end{array}\right], \quad b \ne 0,$

which has eigenvalues $a \pm \mathrm{i}b$.

The Schur decomposition can be computed by the QR algorithm at a cost of about $25n^3$ flops for $Q$ and $T$, or $10n^3$ flops for $T$ only.

In MATLAB, the Schur decomposition is computed with the schur function: the command [Q,T] = schur(A) returns the real Schur form if $A$ is real and otherwise the complex Schur form. The complex Schur form for a real matrix can be obtained with [Q,T] = schur(A,'complex'). The rsf2csf function converts the real Schur form to the complex Schur form. The= ordschur function takes a Schur decomposition and modifies it so that the eigenvalues appear in a specified order along the diagonal of $T$.

# What Is a Permutation Matrix?

A permutation matrix is a square matrix in which every row and every column contains a single $1$ and all the other elements are zero. Such a matrix, $P$ say, is orthogonal, that is, $P^TP = PP^T = I_n$, so it is nonsingular and has determinant $\pm 1$. The total number of $n\times n$ permutation matrices is $n!$.

Premultiplying a matrix by $P$ reorders the rows and postmultiplying by $P$ reorders the columns. A permutation matrix $P$ that has the desired reordering effect is constructed by doing the same operations on the identity matrix.

Examples of permutation matrices are the identity matrix $I_n$, the reverse identity matrix $J_n$, and the shift matrix $K_n$ (also called the cyclic permutation matrix), illustrated for $n = 4$ by

$\notag I_4 = \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 \end{bmatrix}, \qquad J_4 = \begin{bmatrix} 0 & 0 & 0 & 1 \\ 0 & 0 & 1 & 0 \\ 0 & 1 & 0 & 0 \\ 1 & 0 & 0 & 0 \end{bmatrix}, \qquad K_4 = \begin{bmatrix} 0 & 1 & 0 & 0 \\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 \\ 1 & 0 & 0 & 0 \end{bmatrix}.$

Pre- or postmultiplying a matrix by $J_n$ reverses the order of the rows and columns, respectively. Pre- or postmultiplying a matrix by $K_n$ shifts the rows or columns, respectively, one place forward and moves the first one to the last position—that is, it cyclically permutes the rows or columns. Note that $J_n$ is a symmetric Hankel matrix and $K_n$ is a circulant matrix.

An elementary permutation matrix $P$ differs from $I_n$ in just two rows and columns, $i$ and $j$, say. It can be written $P = I_n - (e_i-e_j)(e_i-e_j)^T$, where $e_i$ is the $i$th column of $I_n$. Such a matrix is symmetric and so satisfies $P^2 = I_n$, and it has determinant $-1$. A general permutation matrix can be written as a product of elementary permutation matrices $P = P_1P_2\dots P_k$, where $k$ is such that $\det(P) = (-1)^k$.

It is easy to show that $\det(\lambda I - K_n) = \lambda^n - 1$, which means that the eigenvalues of $K_n$ are $1, w, w^2, \dots, w^{n-1}$, where $w = \exp(2\pi\mathrm{i}/n)$ is the $n$th root of unity. The matrix $K_n$ has two diagonals of $1$s, which move up through the matrix as it is powered: $K_n^i \ne I$ for $i< n$ and $K_n^n = I$. The following animated gif superposes MATLAB spy plots of $K_{64}$, $K_{64}^2$, …, $K_{64}^{64} = I_{64}$.

The shift matrix $K_n$ plays a fundamental role in characterizing irreducible permutation matrices. Recall that a matrix $A\in\mathbb{C}^{n\times n}$ is irreducible if there does not exist a permutation matrix $P$ such that

$\notag P^TAP = \begin{bmatrix} A_{11} & A_{12} \\ 0 & A_{22} \end{bmatrix},$

where $A_{11}$ and $A_{22}$ are square, nonempty submatrices.

Theorem 1. For a permutation matrix $P \in \mathbb{R}^{n \times n}$ the following conditions are equivalent.

• $P$ is irreducible.
• There exists a permutation matrix $Q$ such that $Q^{-1}PQ = K_n$
• The eigenvalues of $P$ are $1, w, w^2, \dots, w^{n-1}$.

One consequence of Theorem 1 is that for any irreducible permutation matrix $P$, $\mathrm{rank}(P - I) = \mathrm{rank}(K_n - I) = n-1$.

The next result shows that a reducible permutation matrix can be expressed in terms of irreducible permutation matrices.

Theorem 2. Every reducible permutation matrix is permutation similar to a direct sum of irreducible permutation matrices.

Another notable permutation matrix is the vec-permutation matrix, which relates $A\otimes B$ to $B\otimes A$, where $\otimes$ is the Kronecker product.

A permutation matrix is an example of a doubly stochastic matrix: a nonnegative matrix whose row and column sums are all equal to $1$. A classic result characterizes doubly stochastic matrices in terms of permutation matrices.

Theorem 3 (Birkhoff). A matrix is doubly stochastic if and only if it is a convex combination of permutation matrices.

In coding, memory can be saved by representing a permutation matrix $P$ as an integer vector $p$, where $p_i$ is the column index of the $1$ within the $i$th row of $P$. MATLAB functions that return permutation matrices can also return the permutation in vector form. Here is an example with the MATLAB lu function that illustrates how permuting a matrix can be done using the vector permutation representation.

>> A = gallery('frank',4), [L,U,P] = lu(A); P
A =
4     3     2     1
3     3     2     1
0     2     2     1
0     0     1     1
P =
1     0     0     0
0     0     1     0
0     0     0     1
0     1     0     0
>> P*A
ans =
4     3     2     1
0     2     2     1
0     0     1     1
3     3     2     1
>> [L,U,p] = lu(A,'vector'); p
p =
1     3     4     2
>> A(p,:)
ans =
4     3     2     1
0     2     2     1
0     0     1     1
3     3     2     1


For more on handling permutations in MATLAB see section 24.3 of MATLAB Guide.

## Notes

For proofs of Theorems 1–3 see Zhang (2011, Sec. 5.6). Theorem 3 is also proved in Horn and Johnson (2013, Thm. 8.7.2).

Permutations play a key role in the fast Fourier transform and its efficient implementation; see Van Loan (1992).

# What Is the Matrix Inverse?

The inverse of a matrix $A\in\mathbb{C}^{n\times n}$ is a matrix $X\in\mathbb{C}^{n\times n}$ such that $AX = I$, where $I$ is the identity matrix (which has ones on the diagonal and zeros everywhere else). The inverse is written as $A^{-1}$. If the inverse exists then $A$ is said to be nonsingular or invertible, and otherwise it is singular.

The inverse $X$ of $A$ also satisfies $XA = I$, as we now show. The equation $AX = I$ says that $Ax_j = e_j$ for $j=1\colon n$, where $x_j$ is the $j$th column of $A$ and $e_j$ is the $j$th unit vector. Hence the $n$ columns of $A$ span $\mathbb{C}^n$, which means that the columns are linearly independent. Now $A(I-XA) = A - AXA = A -A = 0$, so every column of $I - XA$ is in the null space of $A$. But this contradicts the linear independence of the columns of $A$ unless $I - XA = 0$, that is, $XA = I$.

The inverse of a nonsingular matrix is unique. If $AX = AW = I$ then premultiplying by $X$ gives $XAX = XAW$, or, since $XA = I$, $X = W$.

The inverse of the inverse is the inverse: $(A^{-1})^{-1} = A$, which is just another way of interpreting the equations $AX = XA = I$.

## Connections with the Determinant

Since the determinant of a product of matrices is the product of the determinants, the equation $AX = I$ implies $\det(A) \det(X) = 1$, so the inverse can only exist when $\det(A) \ne 0$. In fact, the inverse always exists when $\det(A) \ne 0$.

An explicit formula for the inverse is

$\notag A^{-1} = \displaystyle\frac{\mathrm{adj}(A)}{\det(A)}, \qquad (1)$

where the adjugate $\mathrm{adj}$ is defined by

$\bigl(\mathrm{adj}(A)\bigr)_{ij} = (-1)^{i+j} \det(A_{ji})$

and where $A_{pq}$ denotes the submatrix of $A$ obtained by deleting row $p$ and column $q$. A special case is the formula

$\notag \begin{bmatrix} a & b \\ c& d \end{bmatrix}^{-1} = \displaystyle\frac{1}{ad-bc} \begin{bmatrix} d & -b \\ -c & a \end{bmatrix}.$

The equation $AA^{-1} = I$ implies $\det(A^{-1}) = 1/\det(A)$.

## Conditions for Nonsingularity

The following result collects some equivalent conditions for a matrix to be nonsingular. We denote by $\mathrm{null}(A)$ the null space of $A$ (also called the kernel).

Theorem 1. For $A \in \mathbb{C}^{n \times n}$ the following conditions are equivalent to $A$ being nonsingular:

• $\mathrm{null}(A) = \{0\}$,
• $\mathrm{rank}(A) = n$,
• $Ax=b$ has a unique solution $x$, for any $b$,
• none of the eigenvalues of $A$ is zero,
• $\det(A) \ne 0$.

A useful formula is

$\notag (AB)^{-1} = B^{-1}A^{-1}.$

Here are some facts about the inverses of $n\times n$ matrices of special types.

• A diagonal matrix $D = \mathrm{diag}(d_i)$ is nonsingular if $d_i\ne0$ for all $i$, and $D^{-1} = \mathrm{diag}(d_i^{-1})$.
• An upper (lower) triangular matrix $T$ is nonsingular if its diagonal elements are nonzero, and the inverse is upper (lower) triangular with $(i,i)$ element $t_{ii}^{-1}$.
• If $x,y\in\mathbb{C}^n$ and $y^*A^{-1}x \ne -1$, then $A + xy^*$ is nonsingular and

$\notag \bigl(A + xy^*\bigr)^{-1} = A^{-1} - \displaystyle\frac{A^{-1}x y^* A^{-1}}{1 + y^*A^{-1}x}.$

This is the Sherman–Morrison formula.

## The Inverse as a Matrix Polynomial

The Cayley-–Hamilton theorem says that a matrix satisfies its own characteristic equation, that is, if $p(t) = \det(tI - A) = t^n + c_{n-1} t^{n-1} + \cdots + c_0$, then $p(A) = 0$. In other words, $A^n + c_{n-1} A^{n-1} + \cdots + c_0I = 0$, and if $A$ is nonsingular then multiplying through by $A^{-1}$ gives (since $c_0 = p(0) = (-1)^n\det(A) \ne 0)$

$A^{-1} = -\displaystyle\frac{1}{c_0} (A^{n-1} + c_{n-1} A^{n-2} + \cdots + c_1 I). \qquad (2)$

This means that $A^{-1}$ is expressible as a polynomial of degree at most $n-1$ in $A$ (with coefficients that depend on $A$).

## To Compute or Not to Compute the Inverse

The inverse is an important theoretical tool, but it is rarely necessary to compute it explicitly. If we wish to solve a linear system of equations $Ax = b$ then computing $A^{-1}$ and then forming $x = A^{-1}b$ is both slower and less accurate in floating-point arithmetic than using LU factorization (Gaussian elimination) to solve the system directly. Indeed, for $n = 1$ one would not solve $3x = 1$ by computing $3^{-1} \times 1$.

For sparse matrices, computing the inverse may not even be practical, as the inverse is usually dense.

If one needs to compute the inverse, how should one do it? We will consider the cost of different methods, measured by the number of elementary arithmetic operations (addition, subtraction, division, multiplication) required. Using (1), the cost is that of computing one determinant of order $n$ and $n^2$ determinants of order $n-1$. Since computing a $k\times k$ determinant costs at least $O(k^3)$ operations by standard methods, this approach costs at least $O(n^5)$ operations, which is prohibitively expensive unless $n$ is very small. Instead one can compute an LU factorization with pivoting and then solve the systems $Ax_i = e_i$ for the columns $x_i$ of $A^{-1}$, at a total cost of $2n^3 + O(n^2)$ operations.

Equation (2) does not give a good method for computing $A^{-1}$, because computing the coefficients $c_i$ and evaluating a matrix polynomial are both expensive.

It is possible to exploit fast matrix multiplication methods, which compute the product of two $n\times n$ matrices in $O(n^\alpha)$ operations for some $\alpha < 3$. By using a block LU factorization recursively, one can reduce matrix inversion to matrix multiplication. If we use Strassen’s fast matrix multiplication method, which has $\alpha = \log_2 7 \approx 2.807$, then we can compute $A^{-1}$ in $O(n^{2.807})$ operations.

## Slash Notation

MATLAB uses the backslash and forward slash for “matrix division”, with the meanings $A \backslash B = A^{-1}B$ and $A / B = AB^{-1}$. Note that because matrix multiplication is not commutative, $A \backslash B \ne A / B$, in general. We have $A\backslash I = I/A = A^{-1}$ and $I\backslash A = A/I = A$. In MATLAB, the inverse can be compute with inv(A), which uses LU factorization with pivoting.

## Rectangular Matrices

If $A$ is $m\times n$ then the equation $AX = I_m$ requires $X$ to be $n\times m$, as does $XA = I_n$. Rank considerations show that at most one of these equations can hold if $m\ne n$. For example, if $A = a^*$ is a nonzero row vector, then $AX = 1$ for $X = a/a^*a$, but $XA = aa^*/a^*a\ne I$. This is an example of a generalized inverse.

## An Interesting Inverse

Here is a triangular matrix with an interesting inverse. This example is adapted from the LINPACK Users’ Guide, which has the matrix, with “LINPACK” replacing “INVERSE” on the front cover and the inverse on the back cover.

$\notag \left[\begin{array}{ccccccc} I & N & V & E & R & S & E\\ 0 & N & V & E & R & S & E\\ 0 & 0 & V & E & R & S & E\\ 0 & 0 & 0 & E & R & S & E\\ 0 & 0 & 0 & 0 & R & S & E\\ 0 & 0 & 0 & 0 & 0 & S & E\\ 0 & 0 & 0 & 0 & 0 & 0 & E \end{array}\right]^{-1} = \left[\begin{array}{*{7}{r@{\hspace{4pt}}}} 1/I & -1/I & 0 & 0 & 0 & 0 & 0\\ 0 & 1/N & -1/N & 0 & 0 & 0 & 0\\ 0 & 0 & 1/V & -1/V & 0 & 0 & 0\\ 0 & 0 & 0 & 1/E & -1/E & 0 & 0\\ 0 & 0 & 0 & 0 & 1/R & -1/R & 0\\ 0 & 0 & 0 & 0 & 0 & 1/S & -1/S\\ 0 & 0 & 0 & 0 & 0 & 0 & 1/E \end{array}\right].$

# What Is A\A?

In a recent blog post What is $A\backslash A$?, Cleve Moler asked what the MATLAB operation $A \backslash A$ returns. I will summarize what backslash does in general, for $A \backslash B$ and then consider the case $B = A$.

$A \backslash B$ is a solution, in some appropriate sense, of the equation

$\notag AX = B, \quad A \in\mathbb{C}^{m\times n} \quad X \in\mathbb{C}^{n\times p} \quad B \in\mathbb{C}^{m\times p}. \qquad (1)$

It suffices to consider the case $p = 1$, because backslash treats the columns independently, and we write this as

$\notag Ax = b, \quad A \in\mathbb{C}^{m\times n} \quad x \in\mathbb{C}^{n} \quad b \in\mathbb{C}^{m}.$

The MATLAB backslash operator handles several cases depending on the relative sizes of the row and column dimensions of $A$ and whether it is rank deficient.

## Square Matrix: $m = n$

When $A$ is square, backslash returns $x = A^{-1}b$, computed by LU factorization with partial pivoting (and of course without forming $A^{-1}$). There is no special treatment for singular matrices, so for them division by zero may occur and the output may contain NaNs (in practice, what happens will usually depend on the rounding errors). For example:

>> A = [1 0; 0 0], b = [1 0]', x = A\b
A =
1     0
0     0
b =
1
0
Warning: Matrix is singular to working precision.

x =
1
NaN


Backslash take advantage of various kinds of structure in $A$; see MATLAB Guide (section 9.3) or doc mldivide in MATLAB.

## Overdetermined System: $m > n$

An overdetermined system has no solutions, in general. Backslash yields a least squares (LS) solution, which is unique if $A$ has full rank. If $A$ is rank-deficient then there are infinitely many LS solutions, and backslash returns a basic solution: one with at most $\mathrm{rank}(A)$ nonzeros. Such a solution is not, in general, unique.

## Underdetermined System: $m < n$

An underdetermined system has fewer equations than unknowns, so either there is no solution of there are infinitely many. In the latter case $A\backslash b$ produces a basic solution and in the former case a basic LS solution. Example:

>> A = [1 1 1; 1 1 0]; b = [3 2]'; x = A\b
x =
2.0000e+00
0
1.0000e+00


Another basic solution is $[0~2~1]^T$, and the minimum $2$-norm solution is $[1~1~1]^T$.

## A\A

Now we turn to the special case $A\backslash A$, which in terms of equation (1) is a solution to $AX = A$. If $A = 0$ then $X = I$ is not a basic solution, so $A\backslash A \ne I$; in fact, $0\backslash 0 = 0$ if $m\ne n$ and it is matrix of NaNs if $m = n$.

For an underdetermined system with full-rank $A$, $A\backslash A$ is not necessarily the identity matrix:

>> A = [1 0 1; 0 1 0], X = A\A
A =
1     0     1
0     1     0
X =
1     0     1
0     1     0
0     0     0


But for an overdetermined system with full-rank $A$, $A\backslash A$ is the identity matrix:

>> A'\A'
ans =
1.0000e+00            0
-1.9185e-17   1.0000e+00


## Minimum Frobenius Norm Solution

The MATLAB definition of $A\backslash b$ is a pragmatic one, as it computes a solution or LS solution to $Ax = b$ in the most efficient way, using LU factorization ($m = n$) or QR factorization $(m\ne n$). Often, one wants the solution of minimum $2$-norm, which can be expressed as $A^+b$, where $A^+$ is the pseudoinverse of $A$. In MATLAB, $A^+b$ can be computed by lsqminnorm(A,b) or pinv(A)*b, the former expression being preferred as it avoids the unnecessary computation of $A^+$ and it uses a complete orthogonal factorization instead of an SVD.

When the right-hand side is a matrix, $B$, lsqminnorm(A,B) and pinv(A)*B give the solution of minimal Frobenius norm, which we write as $A \backslash\backslash B$. Then $A\backslash\backslash A = A^+A$, which is the orthogonal projector onto $\mathrm{range}(A^*)$, and it is equal to the identity matrix when $m\ge n$ and $A$ has full rank. For the matrix above:

>> A = [1 0 1; 0 1 0], X = lsqminnorm(A,A)
A =
1     0     1
0     1     0
X =
5.0000e-01            0   5.0000e-01
0   1.0000e+00            0
5.0000e-01            0   5.0000e-01


# What Is the Jordan Canonical Form?

How close can similarity transformations take a matrix towards diagonal form? The answer is given by the Jordan canonical form, which achieves the largest possible number of off-diagonal zero entries (Brualdi, Pei, and Zhan, 2008).

Theorem (Jordan canonical form). Any matrix $A\in\mathbb{C}^{n\times n}$ can be expressed as

\notag \begin{aligned} A &= ZJZ^{-1}, \quad J = \mathrm{diag}(J_1, J_2, \dots, J_p), \\ J_k &= J_k(\lambda_k) = \begin{bmatrix} \lambda_k & 1 & & \\ & \lambda_k & \ddots & \\ & & \ddots & 1 \\ & & & \lambda_k \end{bmatrix} \in \mathbb{C}^{m_k\times m_k}, \label{Jk} \end{aligned}

where $Z$ is nonsingular and $m_1 + m_2 + \cdots + m_p = n$. The matrix $J$ is unique up to the ordering of the blocks $J_k$.

The matrix $J$ is (up to reordering of the diagonal blocks) the Jordan canonical form of $A$ (or the Jordan form, for short).

The bidiagonal matrices $J_k$ are called Jordan blocks. Clearly, the eigenvalues of $J_k$ are $\lambda_k$ repeated $m_k$ times and $J_k$ has a single eigenvector, $e_1\in\mathbb{R}^{m_k}$. Two different Jordan blocks can have the same eigenvalues.

In total, $J$ has $p$ linearly independent eigenvectors, and the same is true of $A$.

The Jordan canonical form is an invaluable tool in matrix analysis, as it provides a concrete way to prove and understand many results. However, the Jordan form can not be reliably computed in finite precision arithmetic, so it is of little use computationally, except in special cases such as when $A$ is Hermitian or normal.

For a Jordan block $J_k = J_k(\lambda_k)\in\mathbb{C}^{m_k\times m_k}$ we have

\notag \begin{aligned} J_k - \lambda_k I &= \begin{bmatrix} 0 & 1 & & \\ & 0 & \ddots & \\ & & \ddots & 1 \\ & & & 0 \end{bmatrix}, \quad (J_k - \lambda_k I)^2 = \begin{bmatrix} 0 & 0 & 1 & & \\ & 0 & 0 & \ddots & \\ & & \ddots & \ddots& 1 \\ & & & \ddots & 0 \\ & & & & 0 \end{bmatrix},\\ \dots,\quad (J_k - \lambda_k I)^{m_k-1} &= \begin{bmatrix} 0 & 0 & \dots & 1 \\ & 0 & \dots & 0 \\ & & \ddots & \vdots \\ & & & 0 \end{bmatrix}, \quad (J_k - \lambda_k I)^{m_k} = 0. \qquad (*)\notag \end{aligned}

The superdiagonal of ones moves up to the right with each increase in the index of the power, until it disappears off the top corner of the matrix.

It is easy to see that $(A - \lambda I_n)^{j} = Z(J - \lambda I_n)^{j} Z^{-1} = Z\mathrm{diag}\bigl((J_k(\lambda_k) - \lambda I_{m_k})^{j}\bigr) Z^{-1}$, and so

$\mathrm{rank}( (A - \lambda I_n)^{j} ) = \sum_{k = 1}^p\mathrm{rank}\bigl( (J_k(\lambda_k) - \lambda I_{m_k})^{j} \bigr).$

For $\lambda = \lambda_k$, these quantities provide information about the size of the Jordan blocks associated with $\lambda_k$. To be specific, let

$\notag d_j = \mathrm{rank}( (A - \lambda_kI_n)^{j}), \quad j\ge 1, \quad \quad d_0 = n$

and

$\notag \omega_j = d_{j-1} - d_j, \quad j \ge 1.$

By considering the equations $(*)$ above, it can be shown that $\omega_j$ is the number of Jordan blocks of size at least $j$ in which $\lambda_k$ appears. Moreover, the number of Jordan blocks of size $j$ is $\omega_j - \omega_{j+1} = d_{j-1} - 2d_j + d_{j+1}$. Therefore if we know the eigenvalues and the ranks of $(A - \lambda_k I_n)^j$ for each eigenvalue $\lambda_k$ and appropriate $j$ then we can determine the Jordan structure. As an important special case, if $\mathrm{rank}(A - \lambda_k I_n) = n-1$ then we know that $\lambda_k$ appears in a single Jordan block. The sequence of $\omega_j$ is known as the Weyr characteristic, and it satisfies $\omega_1 \ge \omega_2 \ge \cdots$.

As an example of a matrix for which we can easily deduce the Jordan form consider the nilpotent matrix $B = \bigl[\begin{smallmatrix} 0_r & I_r\\0_r & 0_r \end{smallmatrix}\bigr]$, for which $B^2 = 0$ and all the eigenvalues are zero. Since $\mathrm{rank}(B) = r$, we have $d_0 = 2r$, $d_1 = r$, and $d_2 = 0$. Hence $\omega_1 = 0$ and $\omega_2 = r$, so there are $r$ $2\times 2$ Jordan blocks. (In fact, $A$ can be permuted into Jordan form by a similarity transformation.)

Here is an example with $A$ the $11\times 11$ matrix anymatrix('core/collatz',11).

$\notag A = \left[\begin{array}{ccccccccccc} 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\ 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\ 0 & 0 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0\\ 0 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\ 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 0\\ 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\ 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1\\ 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0\\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0\\ 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 0\\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \end{array}\right]$

We have $\mathrm{rank}(A) = 10$ and $\mathrm{rank}(A-2I) = 10$, so $0$ and $2$ are simple eigenvalues. All the other eigenvalues are $1$ and they have the following $d_j$ and $\omega_j$ values:

$\notag \begin{array}{cccc} j & d_j &\omega_j &\omega_j - \omega_{j+1}\\\hline 0 & 11 & & \\ 1 & 7 & 4 & 2 \\ 2 & 5 & 2 & 1 \\ 3 & 4 & 1 & 0 \\ 4 & 3 & 1 & 0 \\ 5 & 2 & 1 & 1 \\ 6 & 2 & 0 & 0 \\ \end{array}$

We conclude that the eigenvalue $1$ occurs in one block of order $5$, one block of order $2$, and two blocks of order $1$.

A matrix and its transpose have the same Jordan form. One way to see this is to note that $A = ZJZ^{-1}$ implies

$A^T = Z^{-T}J^TZ^T = Z^{-T}P \cdot PJ^TP \cdot PZ^T = (ZP)^{-T}J \,(ZP)^T,$

where $P$ is the identity matrix with the its columns reversed. A consequence is that $A$ and $A^T$ are similar.

## Real Jordan Form

A version of the Jordan form with $Z$ and $J$ real exists for $A\in\mathbb{R}^{n\times n}$. The main change is how complex eigenvalues are represented. Since the eigenvalues now occur in complex conjugate pairs $\lambda$ and $\overline{\lambda}$, and each of the pair has the same Jordan structure (which follows from the fact that a matrix and its complex conjugate have the same rank), pairs of Jordan blocks corresponding to $\lambda$ and $\overline{\lambda}$ are combined into a real block of twice the size. For example, Jordan blocks

$\notag \begin{bmatrix} \lambda & 1 \\ 0 & \lambda \end{bmatrix}, \; \begin{bmatrix}\,\overline{\lambda} & 1 \\ 0 & \overline{\lambda} \end{bmatrix} \in\mathbb{C}^{2 \times 2}$

become

$\notag \left[\begin{array}{@{\mkern3mu}rr|rr@{\mkern7mu}} a & b & 1 & 0 \\ -b & a & 0 & 1 \\\hline 0 & 0 & a & b \\ 0 & 0 &-b & a \end{array}\right] \in\mathbb{R}^{4 \times 4}$

in the real Jordan form, where $\lambda = a + \mathrm{i} b$. Note that the eigenvalues of $\bigl[\begin{smallmatrix} a & b \\ -b & a \end{smallmatrix}\bigr]$ are $a \pm \mathrm{i} b$.

## Notes

Proofs of the Jordan canonical form and its real variant can be found in many textbooks. See also Brualdi (1987) and Fletcher and Sorensen (1983), who give proofs that go via the Schur decomposition.

# What Is the Second Difference Matrix?

The second difference matrix is the tridiagonal matrix $T_n$ with diagonal elements $2$ and sub- and superdiagonal elements $-1$:

$\notag T_n = \left[ \begin{array}{@{}*{4}{r@{\mskip10mu}}r} 2 & -1 & & & \\ -1 & 2 & -1 & & \\[-5pt] & -1 & 2 & \ddots & \\ & & \ddots & \ddots & -1 \\ & & & -1 & 2 \end{array}\right] \in\mathbb{R}^{n\times n}.$

It arises when a second derivative is approximated by the central second difference $f''(x) \approx (f(x+h) -2 f(x) + f(x-h))/h^2$. (Accordingly, the second difference matrix is sometimes defined as $-T_n$.) In MATLAB, $T_n$ can be generated by gallery('tridiag',n), which is returned as a aparse matrix.

This is Gil Strang’s favorite matrix. The photo, from his home page, shows a birthday cake representation of the matrix.

The second difference matrix is symmetric positive definite. The easiest way to see this is to define the full rank rectangular matrix

$\notag L_n = \begin{bmatrix} 1 & & & \\ -1 & 1 & & \\ & -1 & \ddots & \\ & & \ddots & 1 \\ & & & -1 \end{bmatrix} \in\mathbb{R}^{(n+1)\times n}$

and note that $T_n = L_n^T L_n$. The factorization corresponds to representing a central difference as the product of a forward difference and a backward difference. Other properties of the second difference matrix are that it is diagonally dominant, a Toeplitz matrix, and an $M$-matrix.

## Cholesky Factorization

In an LU factorization $A = LU$ the pivots $u_{ii}$ are $2$, $3/2$, $4/3$, …, $(n+1)/n$. Hence the pivots form a decreasing sequence tending to 1 as $n\to\infty$. The diagonal of the Cholesky factor contains the square roots of the pivots. This means that in the Cholesky factorization $A = R^*R$ with $R$ upper bidiagonal, $r_{nn} \to 1$ and $r_{n,n-1}\to -1$ as $n\to\infty$.

## Determinant, Inverse, Condition Number

Since the determinant is the product of the pivots, $\det(T_n) = n+1$.

The inverse of $T_n$ is full, with $(i,j)$ element $i(n-j+1)/(n+1)$ for $j\ge i$. For example,

$\notag T_5^{-1} = \displaystyle\frac{1}{6} \begin{bmatrix} 5 & 4 & 3 & 2 & 1\\ 4 & 8 & 6 & 4 & 2\\ 3 & 6 & 9 & 6 & 3\\ 2 & 4 & 6 & 8 & 4\\ 1 & 2 & 3 & 4 & 5 \end{bmatrix}.$

The $2$-norm condition number satisfies $\kappa_2(T_n) \sim 4n^2/\pi^2$ (as follows from the formula (1) below for the eigenvalues).

## Eigenvalues and Eigenvectors

The eigenvalues of $T_n$ are

$\notag \mu_k = 2 - 2 \cos( k \phi) = 4 \sin^2\Bigl(\displaystyle\frac{k \phi}{2} \Bigr), \quad k = 1:n, \qquad (1)$

where $\phi = \pi/(n+1)$, with corresponding eigenvector

$\notag v_k = \begin{bmatrix} \sin(k\phi), &\sin(2k\phi), &\dots, &\sin(nk\phi) \end{bmatrix}^T.$

The matrix $Q$ with

$\notag q_{ij} = \Bigl(\displaystyle\frac{2}{n+1}\Bigr)^{1/2} \sin\Bigl( \frac{ij\pi}{n+1} \Bigr)$

is therefore an eigenvector matrix for $T_n$: $Q^*AQ = \mathrm{diag}(\mu_k)$.

## Variations

Various modifications of the second difference matrix arise and similar results can be derived. For example, consider the matrix obtained by changing the $(n,n)$ element to $1$:

$\notag \widetilde{T}_n = \left[ \begin{array}{@{}*{4}{r@{\mskip10mu}}r} 2 & -1 & & & \\ -1 & 2 & -1 & & \\[-5pt] & -1 & 2 & \ddots & \\ & & \ddots & \ddots & -1 \\ & & & -1 & 1 \end{array}\right] \in\mathbb{R}^{n\times n}.$

It can be shown that $\widetilde{T}_n^{-1}$ has $(i,j)$ element $\min(i,j)$ and eigenvalues $4\cos( j \pi/(2n+1))^2$, $j=1:n$.

If we perturb the $(1,1)$ of $\widetilde{T}_n$ to $1$, we obtain a singular matrix, but suppose we perturb the $(1,1)$ element to $3$:

$\notag \widehat{T}_n = \left[ \begin{array}{@{}*{4}{r@{\mskip10mu}}r} 3 & -1 & & & \\ -1 & 2 & -1 & & \\[-5pt] & -1 & 2 & \ddots & \\ & & \ddots & \ddots & -1 \\ & & & -1 & 1 \end{array}\right] \in\mathbb{R}^{n\times n}.$

The inverse is $\widehat{T}_n^{-1} = G/2$, where $G$ with $(i,j)$ element $2\min(i,j)-1$ is a matrix of Givens, and the eigenvalues are $4\cos((2j-1)\pi/(4n))^2$, $j=1:n$.

## Notes

The factorization $T_n = L_n^TL_n$ is noted by Strang (2012).

For a derivation of the eigenvalues and eigenvectors see Todd (1977, p. 155 ff.). For the eigenvalues of $\widetilde{T}_n$ see Fortiana and Cuadras (1997). Givens’s matrix is described by Newman and Todd (1958) and Todd (1977).

## References

This is a minimal set of references, which contain further useful references within.

# What Is the Frank Matrix?

The $n\times n$ upper Hessenberg matrix

$\notag F_n = \left[\begin{array}{*{6}c} n & n-1 & n-2 & \dots & 2 & 1 \\ n-1 & n-1 & n-2 & \dots & 2 & 1 \\ 0 & n-2 & n-2 & \dots & 2 & 1 \\[-3pt] \vdots & 0 & \ddots & \ddots & \vdots & 1 \\[-3pt] \vdots & \vdots & \dots & 2 & 2 & 1 \\ 0 & 0 & \dots & 0 & 1 & 1 \\ \end{array}\right]$

was introduced by Frank in 1958 as a test matrix for eigensolvers.

## Determinant

Taking the Laplace expansion about the first column, we obtain $\det(F_n) = n\det(F_{n-1}) - (n-1)\det(F_{n-1}) = \det(F_{n-1})$, and since $\det(F_1) = 1$ we have $\det(F_n) = 1$.

In MATLAB, the computed determinant of the matrix and its transpose can be far from the exact values of $1$:

>> n = 20; F = anymatrix('gallery/frank',n);
>> [det(F), det(F')]
ans =
1.0000e+00  -1.4320e+01
>> n = 49; F = anymatrix('gallery/frank',n); det(F)
ans =
-1.406934439401568e+45


This behavior illustrates the sensitivity of the determinant rather than numerical instability in the evaluation and it is very dependent on the arithmetic (different results may be obtained in different releases of MATLAB). The sensitivity can be seen by noting that

$\notag \det\bigl(F_n + \epsilon e_1e_n^T\bigr) = 1 + (-1)^{n-1} (n-1)!\epsilon, \qquad (1)$

which means that changing the $(1,n)$ element from 1 to $1+\epsilon$ changes the determinant by $(n-1)!\epsilon$.

## Inverse and Condition Number

It is easily verified that

$\notag F_n = \begin{bmatrix} 1 & -1 & & & \\ & 1 & -1 & & \\ & & 1 & \ddots & \\ & & & \ddots & -1\\ & & & & 1 \end{bmatrix}^{-1} \begin{bmatrix} 1 & & & & \\ n-1 & 1 & & & \\ & n-2& 1 & & \\ & & \ddots& \ddots & \\ & & & 1 & 1 \end{bmatrix} \equiv U^{-1}L. \qquad (2)$

Hence $F_n^{-1} = L^{-1}U$ is lower Hessenberg with integer entries. This factorization provides another way to see that $\det(F_n) = 1$.

From (1) we see that $F_n + \epsilon e_1e_n^T$ is singular for $\epsilon = (-1)^n/(n-1)!$, which implies that

$\notag \kappa(F_n) \ge (n-1)! \|F_n\|$

for any subordinate matrix norm, showing that the condition number grows very rapidly with $n$. In fact, this lower bound is within a factor $3.5$ of the condition number for the $1$-, $2$-, and $\infty$-norms for $n\le 20$.

## Eigenvalues

Denote by $p_n(t) = \det(F_n - tI)$ the characteristic polynomial of $F_n$. By expanding about the first column one can show that

$\label{Fnpn-rec} p_n(t) = (1-t)p_{n-1}(t) - (n-1)t p_{n-2}(t). \qquad (3)$

Using (3), one can show by induction that

$\notag p_n(t^{-1}) = (-1)^n t^{-n} p_n(t).$

This means that the eigenvalues of $F_n$ occur in reciprocal pairs, and hence that $\lambda = 1$ is an eigenvalue when $n$ is odd. It also follows that $p_n$ is palindromic when $n$ is even and anti-palindromic when $n$ is odd. Examples:

>> charpoly( anymatrix('gallery/frank',6) )
ans =
1   -21   120  -215   120   -21     1
>> charpoly( anymatrix('gallery/frank',7) )
ans =
1   -28   231  -665   665  -231    28    -1


Since $F_n$ has nonzero subdiagonal entries, $\mathrm{rank}(F_n - t I) \ge n-1$ for any $t$, and hence $F_n$ is nonderogatory, that is, no eigenvalue appears in more than one Jordan block in the Jordan canonical form. It can be shown that the eigenvalues are real and positive and that they can be expressed in terms of the zeros of Hermite polynomials. Furthermore, the eigenvalues are distinct.

If each eigenvalue of an $n\times n$ matrix undergoes a small perturbation then the determinant, being the product of the eigenvalues, undergoes a perturbation up to about $n$ times as large. From (1) we see that a change to $F_n$ of order $\epsilon$ can perturb $\det(F_n)$ by $(n-1)!\epsilon$, and it follows that some eigenvalues must undergo a large relative perturbation. The condition number of a simple eigenvalue is defined as the reciprocal of the cosine of the angle between its left and right eigenvectors. For the Frank matrix it is the small eigenvalues that are ill conditioned, as shown here for $n = 9$.

>> n = 9; F = anymatrix('gallery/frank',n);
>> [V,D,cond_evals] = condeig(F); evals = diag(D);
>> [~,k] = sort(evals,'descend');
>> [evals(k) cond_evals(k)]
ans =
2.2320e+01   1.9916e+00
1.2193e+01   2.3903e+00
6.1507e+00   1.5268e+00
2.6729e+00   3.6210e+00
1.0000e+00   6.8615e+01
3.7412e-01   1.5996e+03
1.6258e-01   1.1907e+04
8.2016e-02   2.5134e+04
4.4803e-02   1.4762e+04


## Notes

Frank found that $F_n$ “gives our selected procedures difficulties” and that “accuracy was lost in the smaller roots”. The difficulties encountered by Frank’s codes were shown by Wilkinson to be caused by the inherent sensitivity of the eigenvalues to perturbations in the matrix.

## References

This is a minimal set of references, which contain further useful references within.

# What Is the Logarithmic Norm?

The logarithmic norm of a matrix $A\in\mathbb{C}^{n\times n}$ (also called the logarithmic derivative) is defined by

$\notag \mu(A) = \displaystyle\lim_{\epsilon \to 0+} \frac{ \|I + \epsilon A\| - 1}{\epsilon},$

where the norm is assumed to satisfy $\|I\| = 1$.

Note that the limit is taken from above. If we take the limit from below then we obtain a generally different quantity: writing $\delta = -\epsilon$,

$\notag \displaystyle\lim_{\epsilon \to 0-} \frac{ \|I + \epsilon A\| - 1}{\epsilon} = \lim_{\delta \to 0+} \frac{ \|I - \delta A\| - 1}{-\delta} = -\mu(-A).$

The logarithmic norm is not a matrix norm; indeed it can be negative: $\mu(-I) = -1$.

The logarithmic norm can also be expressed in terms of the matrix exponential.

Lemma 1. For $A\in\mathbb{C}^{n\times n}$,

$\notag \mu(A) = \displaystyle\lim_{\epsilon\to 0+} \frac{\log\, \| \mathrm{e}^{\epsilon A}\|} {\epsilon} = \lim_{\epsilon\to 0+} \frac{\| \mathrm{e}^{\epsilon A}\| - 1} {\epsilon}.$

## Properties

We give some basic properties of the logarithmic norm.

It is easy to see that

$\notag -\|A\| \le \mu(A) \le \|A\|. \qquad (1)$

For $z\in\mathbb{C}$, we define $\mathrm{sign}(z) = z/|z|$ for $z\ne 0$ and $\mathrm{sign}(0) = 0$.

Lemma 2. For $A,B\in\mathbb{C}^{n\times n}$ and $\lambda\in\mathbb{C}$,

• $\mu(\lambda A) = |\lambda| \mu\bigl(\mathrm{sign}(\lambda)A\bigr)$,
• $\mu(A + \lambda I) = \mu(A) + \mathrm{Re}\,\lambda$,
• $\mu(A + B ) \le \mu(A) + \mu(B)$,
• $|\mu(A) - \mu(B)| \le \|A - B\|$.

The next result shows the crucial property that $\mu(A)$ features in an easily evaluated bound for the norm of $\mathrm{e}^{At}$ and that, moreover, $\mu(A)$ is the smallest constant that can appear in this bound.

Theorem 3. For $A\in\mathbb{C}^{n\times n}$ and a consistent matrix norm,

$\notag \|\mathrm{e}^{At}\| \le \mathrm{e}^{\mu(A)t}, \quad t\ge0. \qquad (2)$

Moreover,

$\notag \mu(A) = \min\{\, \theta\in\mathbb{R}: \|\mathrm{e}^{At}\| \le \mathrm{e}^{\theta t} ~\mathrm{for~all}~t\ge 0 \,\}.$

Proof. Given any $\delta > 0$, by Lemma 1 there exists $h > 0$ such that

$\notag \displaystyle\frac{ \| \mathrm{e}^{At}\| - 1}{t} - \mu(A) < \delta, \quad t\in[0,h],$

or

$\notag \| \mathrm{e}^{At}\| \le 1 + (\mu(A) + \delta)t \le \mathrm{e}^{(\mu(A) + \delta)t}, \quad t\in[0,h]$

(since $\mathrm{e}^x \ge 1+x$ for all $x$). Then for any integer $k$, $\|\mathrm{e}^{Atk}\| = \|( \mathrm{e}^{At})^k\| \le \| \mathrm{e}^{At}\|^k \le \mathrm{e}^{(\mu(A) + \delta)tk}$, and hence $\|\mathrm{e}^{At}\| \le \mathrm{e}^{(\mu(A) + \delta)t}$ holds for all $t\in[0,\infty)$. Since $\delta$ is arbitrary, it follows that $\|\mathrm{e}^{At}\| \le \mathrm{e}^{\mu(A)t}$.

Finally, if $\| \mathrm{e}^{At}\| \le \mathrm{e}^{\theta t}$ for all $t\ge 0$ then $(\| \mathrm{e}^{At}\| -1)/t\le (\mathrm{e}^{\theta t}-1)/t$ for all $t\ge0$ and taking $\lim_{t\to 0+}$ we conclude that $\mu(A) \le (d/dt) \mathrm{e}^{\theta t}\mid_{t = 0} \,= \theta$.

The logarithmic norm was introduced by Dahlquist (1958) and Lozinskii (1958) in order to obtain error bounds for numerical methods for solving differential equations. The bound (2) can alternatively be proved by using differential inequalities (see Söderlind (2006)). Not only is (2) sharper than $\|\mathrm{e}^{At}\| \le \mathrm{e}^{\|A\|t}$, but $\mathrm{e}^{\|A\|t}$ is increasing in $t$ while $\mathrm{e}^{\mu(A)t}$ potentially decays, since $\mu(A) < 0$ is possible.

The spectral abscissa is defined by

$\notag \alpha(A) = \max \{\, \mathrm{Re} \lambda : \lambda \in \Lambda(A)\,\},$

where $\Lambda(A)$ denotes the spectrum of $A$ (the set of eigenvalues). Whereas the norm bounds the spectral radius ($\rho(A) \le \|A\|$), the logarithmic norm bounds the spectral abscissa, as shown by the next result.

Theorem 4. For $A\in\mathbb{C}^{n\times n}$ and a consistent matrix norm,

$\notag -\mu(-A) \le \alpha(A) \le \mu(A).$

Combining (1) with (2) gives

$\notag -\|A\| \le -\mu(-A) \le \alpha(A) \le \mu(A) \le \|A\|.$

In view of Lemma 1, the logarithmic norm $\mu(A)$ can be expressed as the one-sided derivative of $\|\mathrm{e}^{tA}\|$ at $t = 0$, so $\mu(A)$ determines the initial rate of change of $\|\mathrm{e}^{tA}\|$ (as well as providing the bound $\mathrm{e}^{\mu(A)t}$ for all $t$). The long-term behavior is determined by the spectral abscissa $\alpha(A)$, since $\|\mathrm{e}^{tA}\| \to 0$ as $t\to\infty$ if and only if $\alpha(A) < 0$, which can be proved using the Jordan canonical form.

The next result provides a bound on the norm of the inverse of a matrix in terms of the logarithmic norm.

Theorem 5. For a nonsingular matrix $A\in\mathbb{C}^{n\times n}$ and a subordinate matrix norm, if $\mu(A) < 0$ or $\mu(-A) < 0$ then

$\notag \|A^{-1}\| \le \displaystyle\frac{1}{\max\bigl(-\mu(A),-\mu(-A)\bigr)}. \qquad (3)$

## Formulas for Logarithmic Norms

Explicit formulas are available for the logarithmic norm s corresponding to the $1$, $2$, and $\infty$-norms.

Theorem 6. For $A\in\mathbb{C}^{n\times n}$,

\notag \begin{aligned} \mu_1(A) &= \max_j \biggl( \sum_{i\ne j} |a_{ij}| + \mathrm{Re}\, a_{jj} \biggr ),\\ \mu_{\infty}(A) &= \max_i \biggl( \sum_{j\ne i} |a_{ij}| + \mathrm{Re}\, a_{ii} \biggr ),\\ \mu_2(A) &= \lambda_{\max}\Bigl( \frac{A+A^*}{2} \Bigr), \qquad (4) \end{aligned}

where $\lambda_{\max}$ denotes the largest eigenvalue of a Hermitian matrix.

Proof. We have

\notag \begin{aligned} \mu_{\infty}(A) &= \lim_{\epsilon \to 0+} \frac{ \|I + \epsilon A\|_{\infty} - 1}{\epsilon}\\ &= \lim_{\epsilon \to 0+} \frac{ \max_i \bigl(\sum_{j\ne i} |\epsilon a_{ij}| + |1 + \epsilon a_{ii}| \bigr) -1}{\epsilon}\\ &= \lim_{\epsilon \to 0+} \frac{ \max_i \bigl(\sum_{j\ne i} |\epsilon a_{ij}| + \sqrt{(1 + \epsilon\mathrm{Re}\, a_{ii})^2 + (\epsilon\mathrm{Im}\,a_{ii})^2}\, \bigr) -1}{\epsilon}\\ &= \lim_{\epsilon \to 0+} \frac{ \max_i \bigl( \sum_{j\ne i} |\epsilon a_{ij}| + \epsilon\mathrm{Re}\, a_{ii} + O(\epsilon^2) \bigr)}{\epsilon}\\ &= \max_i \biggl( \sum_{j\ne i} |a_{ij}| + \mathrm{Re}\, a_{ii} \biggr ). \end{aligned}

The formula for $\mu_1(A)$ follows, since $\|A\|_1 = \|A^*\|_\infty$ implies $\mu_1(A) = \mu_{\infty}(A^*)$. For the $2$-norm, using $\|A\|_2 = \rho(A^*A)^{1/2}$, we have

\notag \begin{aligned} \mu_2(A) &= \lim_{\epsilon \to 0+} \frac{ \|I + \epsilon A\|_2 - 1}{\epsilon}\\ &= \lim_{\epsilon \to 0+} \frac{ \rho\bigl( I + \epsilon(A+A^*) + \epsilon^2 A^*A\bigr)^{1/2} -1} {\epsilon}\\ &= \lim_{\epsilon \to 0+} \frac{ 1 + \frac{1}{2}\epsilon\lambda_{\max}(A+A^*) + O(\epsilon^2) -1} {\epsilon}\\ &= \lambda_{\max}\Bigl( \frac{A+A^*}{2} \Bigr). \end{aligned}

As a special case of (4), if $A$ is normal, so that $A = QDQ^*$ with $Q$ unitary and $D = \mathrm{diag}(\lambda_i)$, then $\mu_2(A) = \max_i (\lambda_i + \overline{\lambda_i})/2 = \max_i \mathrm{Re} \lambda_i = \alpha(A)$.

We can make some observations about $\mu_\infty(A)$ for $A\in\mathbb{R}^{n\times n}$.

• If $A\ge 0$ then $\mu_\infty(A) = \|A\|_\infty$.
• $\mu_\infty(A) < 0$ if and only if $a_{ii} < 0$ for all $i$ and $A$ is strictly row diagonally dominant.
• For the $\infty$-norm the bound (3) is the same as a bound based on diagonal dominance except that it is applicable only when the diagonal is one-signed.

For a numerical example, consider the $n\times n$ tridiagonal matrix anymatrix('gallery/lesp'), which has the form illustrated for $n = 6$ by

$\notag A_6 = \left[\begin{array}{cccccc} -5 & 2 & 0 & 0 & 0 & 0\\ \frac{1}{2} & -7 & 3 & 0 & 0 & 0\\ 0 & \frac{1}{3} & -9 & 4 & 0 & 0\\ 0 & 0 & \frac{1}{4} & -11 & 5 & 0\\ 0 & 0 & 0 & \frac{1}{5} & -13 & 6\\ 0 & 0 & 0 & 0 & \frac{1}{6} & -15 \end{array}\right].$

We find that $\alpha(A_6) = -4.55$ and $\mu_2(A_6) = -4.24$, and it is easy to see that $\mu_1(A_n) = -4.5$ and $\mu_{\infty}(A_n) = -3$ for all $n$. Therefore Theorem 4 shows that $\mathrm{e}^{At}\to 0$ as $t\to \infty$ and $\mu_1$ gives a faster decaying bound than $\mu_2$ and $\mu_\infty$.

Now consider the subordinate matrix norm $\|\cdot\|_G$ based on the vector norm $\|x\|_G = (x^*Gx)^{1/2}$, where $G$ is a Hermitian positive definite matrix. The corresponding logarithmic norm $\mu_G$ can be expressed as the largest eigenvalue of a Hermitian definite generalized eigenvalue problem.

Theorem 7. For $A\in\mathbb{C}^{n\times n}$,

$\notag \mu_G(A) = \max\{\, \lambda: \det(GA + A^*G - 2\lambda G) = 0 \,\}.$

Theorem 7 allows us to make a connection with the theory of ordinary differential equations (ODEs)

$\notag y' = f(t,y), \quad f: \mathbb{R} \times \mathbb{R}^n\to \mathbb{R}^n. \qquad (5)$

Let $G\in\mathbb{R}^{n\times n}$ be symmetric positive definite and consider the inner product $\langle x, y \rangle = x^*Gy$ and the corresponding norm defined by $\|x\|_G^2 = \langle x, x \rangle = (x^*Gx)^{1/2}$. It can be shown that for $A\in\mathbb{R}^{n\times }$,

$\notag \mu_G(A) = \max_x \displaystyle\frac{\langle Ax, x\rangle}{\|x\|_G^2}. \qquad (6)$

The function $f$ satisfies a one-sided Lipschitz condition if there is a function $v(t)$ such that

$\langle f(t,y) - f(t,z), y - z \rangle \le v(t) \|y-z\|^2$

for all $y,z$ in some region and all $a\le t \le b$. For the linear differential equation with $f(t,y) = A(t)y$ in (5), using (6) we obtain

$\langle f(t,y) - f(t,z), y - z \rangle = \langle A(t)(y-z), y - z \rangle \le \mu_G(A(t)) \|y-z\|_G^2,$

and so the logarithmic norm $\mu_G(A(t))$ can be taken as a one-sided Lipschitz constant. This observation leads to results on contractivity of ODEs; see Lambert (1991) for details.

## Notes

For more on the use of the logarithmic norm in differential equations see Coppel (1965) and Söderlind (2006).

The proof of Theorem 3 is from Hinrichsen and Pritchard (2005).

## References

This is a minimal set of references, which contain further useful references within.

• W. A. Coppel, Stability and Asymptotic Behavior of Differential Equations}. D. C. Heath and Company, Boston, MA. USA, 1965.
• Germund Dahlquist. Stability and Error Bounds in the Numerical Integration of Ordinary Differential Equations. PhD thesis, Royal Inst. of Technology, Stockholm, Sweden, 1958.
• Diederich Hinrichsen and Anthony J. Pritchard. Mathematical Systems Theory I. Modelling, State Space Analysis, Stability and Robustness. Springer-Verlag, Berlin, Germany, 2005.
• J. D. Lambert. Numerical Methods for Ordinary Differential Systems. The Initial Value Problem. John Wiley, Chichester, 1991.
• Gustaf Söderlind, The Logarithmic Norm. History and Modern Theory. BIT, 46(3):631–652, 2006.
• Torsten Ström. On Logarithmic Norms. SIAM J. Numer. Anal., 12(5):741–753, 1975.

# What Is a Tridiagonal Matrix?

A tridiagonal matrix is a square matrix whose elements are zero away from the main diagonal, the subdiagonal, and the superdiagonal. In other words, it is a banded matrix with upper and lower bandwidths both equal to $1$. It has the form

$\notag A = \begin{bmatrix} d_1 & e_1 & & &\\ c_2 & d_2 & e_2 & &\\ & c_3 & \ddots & \ddots &\\ & & \ddots & \ddots & e_{n-1}\\ & & & c_n & d_n \end{bmatrix} \in\mathbb{C}^{n\times n}.$

An important example is the matrix $T_n$ that arises in discretizating the Poisson partial differential equation by a standard five-point operator, illustrated for $n = 5$ by

$\notag T_5 = \left[ \begin{array}{@{}*{4}{r@{\mskip10mu}}r} 4 & -1 & & & \\ -1 & 4 & -1 & & \\ & -1 & 4 & -1 & \\ & & -1 & 4 & -1 \\ & & & -1 & 4 \end{array}\right].$

It is symmetric positive definite, diagonally dominant, a Toeplitz matrix, and an $M$-matrix.

Tridiagonal matrices have many special properties and various algorithms exist that exploit their structure.

## Symmetrization

It can be useful to symmetrize a matrix by transforming it with a diagonal matrix. The next result shows when symmetrization is possible by similarity.

Theorem 1. If $A\in\mathbb{R}^{n\times n}$ is tridiagonal and $c_i e_{i-1} > 0$ for all $i$ then there exists a diagonal $D\in\mathbb{R}^{n\times n}$ with positive diagonal elements such that $D^{-1}AD$ is symmetric, with $(i-1,i)$ element $(c_ie_{i-1})^{1/2}$.

Proof. Let $D = \mathrm{diag}(\omega_i)$. Equating $(i,i-1)$ and $(i-1,i)$ elements in the matrix $D^{-1}AD$ gives

$\notag c_i \displaystyle\frac{\omega_{i-1}}{\omega_i} = e_{i-1} \displaystyle\frac{\omega_i}{\omega_{i-1}}, \quad i = 2:n, \qquad(1)$

or

$\notag \biggl(\displaystyle\frac{\omega_{i-1}}{\omega_i}\biggr)^2 = \frac{e_{i-1} }{c_i}, \quad i = 2:n. \qquad (2)$

As long as $c_i e_{i-1} > 0$ for all $i$ we can set $\omega_1 = 1$ and solve (2) to obtain real, positive $\omega_i$, $i = 2:n$. The formula for the off-diagonal elements of the symmetrized matrix follows from (1) and (2).

## LU Factorization

The LU factors of a tridiagonal matrix are bidiagonal:

$\notag L = \begin{bmatrix} 1 & & & & \\ \ell_2 & 1 & & & \\ & \ell_3 & 1 & & \\ & & \ddots & \ddots & \\ & & & \ell_n & 1 \end{bmatrix}, \quad U = \begin{bmatrix} u_1 & e_1 & & & \\ & u_2 & e_2 & & \\ & & \ddots & \ddots & \\ & & & \ddots & e_{n-1}\\ & & & & u_n \end{bmatrix}. \qquad (3)$

The equation $A = LU$ gives the recurrence

$\notag u_1 = d_1, \qquad \ell_i = c_i/u_{i-1}, \quad u_i = d_i - \ell_i e_{i-1}, \quad i=2\colon n. \qquad (4)$

The recurrence breaks down with division by zero if one of the leading principal submatrices $A(1:k,1:k)$, $k = 1:n-1$, is singular. In general, partial pivoting must be used to ensure existence and numerical stability, giving a factorization $PA = LU$ where $L$ has at most two nonzeros per column and $U$ has an extra superdiagonal. The growth factor $\rho_n$ is easily seen to be bounded by $2$.

For a tridiagonal Toeplitz matrix

$\notag T_n(c,d,e) = \begin{bmatrix} d & e & & \\ c & d & \ddots & \\ & \ddots & \ddots & e \\ & & c & d \end{bmatrix} \in\mathbb{C}^{n\times n} \qquad (5)$

the elements of the LU factors converge as $n\to\infty$ if $A$ is diagonally dominant.

Theorem 2. Suppose that $T_n(c,d,e)$ has an LU factorization with LU factors (3) and that $ce > 0$ and $|d| \ge 2\sqrt{ce}$. Then $|u_j|$ decreases monotonically and

$\notag \lim_{n\to \infty}u_n = \frac{1}{2} \Bigl(d + \mathrm{sign}(d) \sqrt{ d^2 - 4ce } \Bigr).$

From (4), it follows that under the conditions of Theorem 2, $|\ell_i|$ increases monotonically and $\lim_{n\to\infty}\ell_n = e/\lim_{n\to\infty}u_n$. Note that the conditions of Theorem 2 are satisfied if $A$ is diagonally dominant by rows, since $ce> 0$ implies $d \ge c + e \ge 2\sqrt{ce}$. Note also that if we symmetrize $A$ using Theorem 1 then we obtain the matrix $T_n(\sqrt{ce},d, \sqrt{ce})$, which is irreducibly diagonally dominant and hence positive definite if $d > 0$.

## Inverse

The inverse of a tridiagonal matrix is full, in general. For example,

$\notag T_5(-1,3,-1)^{-1} = \left[\begin{array}{rrrrr} 3 & -1 & 0 & 0 & 0\\ -1 & 3 & -1 & 0 & 0\\ 0 & -1 & 3 & -1 & 0\\ 0 & 0 & -1 & 3 & -1\\ 0 & 0 & 0 & -1 & 3 \end{array}\right]^{-1} = \frac{1}{144} \left[\begin{array}{ccccc} 55 & 21 & 8 & 3 & 1 \\ 21 & 63 & 24 & 9 & 3 \\ 8 & 24 & 64 & 24 & 8 \\ 3 & 9 & 24 & 63 & 21\\ 1 & 3 & 8 & 21 & 55 \end{array}\right].$

Since an $n\times n$ tridiagonal matrix depends on only $3n-2$ parameters, the same must be true of its inverse, meaning that there must be relations between the elements of the inverse. Indeed, in $T_5(-1,3,-1)^{-1}$ any $2\times 2$ submatrix whose elements lie in the upper triangle is singular, and the $(1:3,3:5)$ submatrix is also singular. The next result explains this special structure. We note that a tridiagonal matrix is irreducible if $a_{i+1,i}$ and $a_{i,i+1}$ are nonzero for all $i$.

Theorem 3. If $A\in\mathbb{C}^{n\times n}$ is tridiagonal, nonsingular, and irreducible then there are vectors $u$, $v$, $x$, and $y$, all of whose elements are nonzero, such that

$\notag (A^{-1})_{ij} = \begin{cases} u_iv_j, & i\le j,\\ x_iy_j, & i\ge j.\\[4pt] \end{cases}$

The theorem says that the upper triangle of the inverse agrees with the upper triangle of a rank-$1$ matrix ($uv^T$) and the lower triangle of the inverse agrees with the lower triangle of another rank-$1$ matrix ($xy^T$). This explains the singular submatrices that we see in the example above.

If a tridiagonal matrix $A$ is reducible with $a_{k,k+1} = 0$ then it has the block form

$\notag \begin{bmatrix} A_{11} & 0 \\ A_{21} & A_{22} \end{bmatrix},$

where $A_{21} = a_{k+1,k}e_1e_k^T$, and so

$\notag \begin{bmatrix} A_{11}^{-1} & 0 \\ - A_{22}^{-1} A_{21} A_{11}^{-1} & A_{22}^{-1} \end{bmatrix},$

in which the $(2,1)$ block is rank $1$ if $a_{k+1,k}\ne 0$. This blocking can be applied recursively until Theorem 1 can be applied to all the diagonal blocks.

The inverse of the Toeplitz tridiagonal matrix $T_n(a,b,c)$ is known explicitly; see Dow (2003, Sec. 3.1).

## Eigenvalues

The most widely used methods for finding eigenvalues and eigenvectors of Hermitian matrices reduce the matrix to tridiagonal form by a finite sequence of unitary similarity transformations and then solve the tridiagonal eigenvalue problem. Tridiagonal eigenvalue problems also arise directly, for example in connection with orthogonal polynomials and special functions.

The eigenvalues of the Toeplitz tridiagonal matrix $T_n(c,d,e)$ in (5) are given by

$\notag d + 2 (ce)^{1/2} \cos\biggl( \displaystyle\frac{k \pi}{n+1} \biggr), \quad k = 1:n. \qquad (6)$

The eigenvalues are also known for certain variations of the symmetric matrix $T_n(c,d,c)$ in which the $(1,1)$ and $(n,n)$ elements are modified (Gregory and Karney, 1969).

Some special results hold for the eigenvalues of general tridiagonal matrices. A matrix is derogatory if an eigenvalue appears in more than one Jordan block in the Jordan canonical form, and nonderogatory otherwise.

Theorem 4. If $A\in\mathbb{C}^{n\times n}$ is an irreducible tridiagonal matrix then it is nonderogatory.

Proof. Let $G = A - \lambda I$, for any $\lambda$. The matrix $G(1:n-1,2:n)$ is lower triangular with nonzero diagonal elements $e_1, \dots, e_{n-1}$ and hence it is nonsingular. Therefore $G$ has rank at least $n-1$ for all $\lambda$. If $A$ were derogatory then the rank of $G$ would be at most $n-2$ when $\lambda$ is an eigenvalue, so $A$ must be nonderogatory.

Corollary 5. If $A\in\mathbb{R}^{n\times n}$ is tridiagonal with $c_i e_{i-1} > 0$ for all $i$ then the eigenvalues of $A$ are real and simple.

Proof. By Theorem 1 the eigenvalues of $A$ are those of the symmetric matrix $S = D^{-1}AD$ and so are real. The matrix $S$ is tridiagonal and irreducible so it is nonderogatory by Theorem 4, which means that its eigenvalues are simple because it is symmetric.

The formula (6) confirms the conclusion of Corollary 5 for tridiagonal Toeplitz matrices.

Corollary 5 guarantees that the eigenvalues are distinct but not that they are well separated. The spacing of the eigenvalues in (6) clearly reduces as $n$ increases. Wilkinson constructed a symmetric tridiagonal matrix called $W_n^+$, defined by

\notag \begin{aligned} d_i &= \frac{n+1}{2} - i = d_{n-i+1}, \quad i = 1:n/2, \qquad d_{(n+1)/2} = 0 \quad \mathrm{if}~n~\mathrm{is~odd}, \\ c_i &= e_{i-1} = 1. \end{aligned}

For example,

$\notag W_5^+ = \left[\begin{array}{ccccc} 2 & 1 & 0 & 0 & 0\\ 1 & 1 & 1 & 0 & 0\\ 0 & 1 & 0 & 1 & 0\\ 0 & 0 & 1 & 1 & 1\\ 0 & 0 & 0 & 1 & 2 \end{array}\right].$

Here are the two largest eigenvalues of $W_{21}^+$, as computed by MATLAB.

>> A = anymatrix('matlab/wilkinson',21);
>> e = eig(A); e([20 21])
ans =
10.746194182903322
10.746194182903393


These eigenvalues (which are correct to the digits shown) agree almost to the machine precision.

## Notes

Theorem 2 is obtained for symmetric matrices by Malcolm and Palmer (1974), who suggest saving work in computing the LU factorization by setting $u_j = u_k$ for $j > k$ once $u_k$ is close enough to the limit.

A sample reference for Theorem 3 is Ikebe (1979), which is one of many papers on inverses of banded matrices.

The eigenvectors of a symmetric tridiagonal matrix satisfy some intricate relations; see Parlett (1998, sec. 7.9).

## References

This is a minimal set of references, which contain further useful references within.

# What Is a Blocked Algorithm?

In numerical linear algebra a blocked algorithm organizes a computation so that it works on contiguous chunks of data. A blocked algorithm and the corresponding unblocked algorithm (with blocks of size $1$) are mathematically equivalent, but the blocked algorithm is generally more efficient on modern computers.

A simple example of blocking is in computing an inner product $s = x^Ty$ of vectors $x,y\in\mathbb{R}^n$. The unblocked algorithm

1. $s = x_1y_1$
2. for $i = 2\colon n$
3.    $s = s + x_ky_k$
4. end

can be expressed in blocked form, with block size $b$, as

1. for $i=1\colon n/b$
2.    $s_i = \sum_{j=(i-1)b+1}^{ib} x_jy_j$ % Compute by the unblocked algorithm.
3. end
4. $s = \sum_{i=1}^{n/b} s_i$

The sums of $b$ terms in line 2 of the blocked algorithm are independent and could be evaluated in parallel, whereas the unblocked form is inherently sequential.

To see the full benefits of blocking we need to consider an algorithm operating on matrices, of which matrix multiplication is the most important example. Suppose we wish to compute the product $C = AB$ of $n\times n$ matrices $A$ and $B$. The natural computation is, from the definition of matrix multiplication, the “point algorithm”

1. $C = 0$
2. for $i=1\colon n$
3.    for $j=1\colon n$
4.      for $k=1\colon n$
5.        $c_{ij} = c_{ij} + a_{ik}b_{kj}$
6.      end
7.   end
8. end

Let $A = (A_{ij})$, $B = (B_{ij})$, and $C = (C_{ij})$ be partitioned into blocks of size $b$, where $r = n/b$ is assumed to be an integer. The blocked computation is

1. $C = 0$
2. for $i=1\colon r$
3.    for $j=1\colon r$
4.      for $k=1\colon r$
5.        $C_{ij} = C_{ij} + A_{ik}B_{kj}$
6.      end
7.    end
8. end

On a computer with a hierarchical memory the blocked form can be much more efficient than the point form if the blocks fit into the high speed memory, as much less data transfer is required. Indeed line 5 of the blocked algorithm performs $O(b^3)$ flops on about $O(n^2)$ data, whereas the point algorithm performs $O(1)$ flops on $O(1)$ data on line 5, or $O(n)$ flops on $O(n)$ data if we combine lines 4–6 into a vector inner product. It is the $O(b)$ flops-to-data ratio that gives the blocked algorithm its advantage, because it masks the memory access costs.

The LAPACK (first released in 1992) was the first program library to systematically use blocked algorithms for a wide range of linear algebra computations.

An extra advantage that blocked algorithms have over unblocked algorithms is a reduction in the error constant in a rounding error bound by a factor $b$ or more and, typically, a reduction in the actual error (Higham, 2021).

The adjective “block” is sometimes used in place of “blocked”, but we prefer to reserve “block” for the meaning described in the next section.

## Block Algorithms

A block algorithm is a generalization of a scalar algorithm in which the basic scalar operations become matrix operations ($\alpha+\beta$, $\alpha\beta$, and $\alpha/\beta$ become $A+B$, $AB$, and $AB^{-1}$). It usually computes a block factorization, in which a matrix property based on the nonzero structure becomes the corresponding property blockwise; in particular, the scalars 0 and 1 become the zero matrix and the identity matrix, respectively.

An example of a block factorization is block LU factorization. For a $4\times 4$ matrix $A$ an LU factorization can be written in the form

$\notag A = \left[ \begin{tabular}{cc|cc} 1 & & & \\ x & 1 & & \\ \hline x & x & 1 & \\ x & x & x & 1 \end{tabular} \right] \left[ \begin{tabular}{cc|cc} x & x & x & x \\ & x & x & x \\ \hline & & x & x \\ & & & x \end{tabular} \right].$

A block LU factorization has the form

$\notag A = \left[ \begin{tabular}{cc|cc} 1 & 0 & & \\ 0 & 1 & & \\ \hline x & x & 1 & 0 \\ x & x & 0 & 1 \end{tabular} \right] \left[ \begin{tabular}{cc|cc} x & x & x & x \\ x & x & x & x \\ \hline & & x & x \\ & & x & x \end{tabular} \right].$

Clearly, these are different factorizations. In general, a block LU factorization has the form $A = LU$ with $L$ block lower triangular, with identity matrices on the diagonal, and $U$ block upper triangular. A blocked algorithm for computing the LU factorization and a block algorithm for computing the block LU factorization are readily derived.

The adjective “block” also applies to a variety of matrix properties, for which there are often special-purpose block algorithms. For example, the matrix

$\notag \begin{bmatrix} A_{11} & A_{12} & & & \\ A_{21} & A_{22} & A_{23} & & \\ & \ddots & \ddots & \ddots & \\ & & \ddots & \ddots & A_{n-1,1} \\ & & & A_{n,n-1} & A_{n,n} \end{bmatrix}$

is a block tridiagonal matrix, and a block Toeplitz matrix has constant block diagonals:

$\notag \begin{bmatrix} A_1 & A_2 & \dots & \dots & A_n \\ A_{-1} & A_1 & A_{2} & & \vdots \\ \vdots & A_{-1} & \ddots & \ddots & \vdots \\ \vdots & & \ddots & \ddots & A_2 \\ A_{1-n}& \dots & \dots & A_{-1} & A_1 \end{bmatrix}.$

One can define block diagonal dominance as a generalization of diagonal dominance. A block Householder matrix generalizes a Householder matrix: it is a perturbation of the identity matrix by a matrix of rank greater than or equal to $1$.

## References

This is a minimal set of references, which contain further useful references within.