# Why does this matrix give the derivative of a function?

I happened to stumble upon the following matrix: $$A = \begin{bmatrix} a & 1 \\ 0 & a \end{bmatrix}$$

And after trying a bunch of different examples, I noticed the following remarkable pattern. If $P$ is a polynomial, then: $$P(A)=\begin{bmatrix} P(a) & P'(a) \\ 0 & P(a) \end{bmatrix}$$

Where $P'(a)$ is the derivative evaluated at $a$.

Futhermore, I tried extending this to other matrix functions, for example the matrix exponential, and wolfram alpha tells me: $$\exp(A)=\begin{bmatrix} e^a & e^a \\ 0 & e^a \end{bmatrix}$$ and this does in fact follow the pattern since the derivative of $e^x$ is itself!

Furthermore, I decided to look at the function $P(x)=\frac{1}{x}$. If we interpret the reciprocal of a matrix to be its inverse, then we get: $$P(A)=\begin{bmatrix} \frac{1}{a} & -\frac{1}{a^2} \\ 0 & \frac{1}{a} \end{bmatrix}$$ And since $f'(a)=-\frac{1}{a^2}$, the pattern still holds!

After trying a couple more examples, it seems that this pattern holds whenever $P$ is any rational function.

I have two questions:

1. Why is this happening?

2. Are there any other known matrix functions (which can also be applied to real numbers) for which this property holds?

• This is related to the 'dual numbers': interpret this matrix as $A = a + \epsilon$, where $\epsilon^2=0$ and then $f(A) = f(a+\epsilon) = f(a) + \epsilon f'(a) + \tfrac12 \epsilon^2 f''(a) + \dots = f(a) + \epsilon f'(a)$. Nov 22, 2015 at 22:44
• Very closely related, in fact, since the dual numbers $\mathbb{R}[\epsilon]$ are isomorphic (as a topological ring) to the matrix ring $\mathbb{R}[A]$.
– user14972
Nov 23, 2015 at 7:36

If $$A = \begin{bmatrix} a & 1 \\ 0 & a \end{bmatrix}$$ then by induction you can prove that $$A^n = \begin{bmatrix} a^n & n a^{n-1} \\ 0 & a^n \end{bmatrix} \tag 1$$ for $n \ge 1$. If $f$ can be developed into a power series $$f(z) = \sum_{n=0}^\infty c_n z^n$$ then $$f'(z) = \sum_{n=1}^\infty n c_n z^{n-1}$$ and it follows that $$f(A) = \sum_{n=0}^\infty c_n A^n = I + \sum_{n=1}^\infty c_n \begin{bmatrix} a^n & n a^{n-1} \\ 0 & a^n \end{bmatrix} = \begin{bmatrix} f(a) & f'(a) \\ 0 & f(a) \end{bmatrix} \tag 2$$ From $(1)$ and $$A^{-1} = \begin{bmatrix} a^{-1} & -a^{-2} \\ 0 & a^{-1} \end{bmatrix}$$ one gets $$A^{-n} = \begin{bmatrix} a^{-1} & -a^{-2} \\ 0 & a^{-1} \end{bmatrix}^n = (-a^{-2})^{n} \begin{bmatrix} -a & 1 \\ 0 & -a \end{bmatrix}^n \\ = (-1)^n a^{-2n} \begin{bmatrix} (-a)^n & n (-a)^{n-1} \\ 0 & (-a)^n \end{bmatrix} = \begin{bmatrix} a^{-n} & -n a^{-n-1} \\ 0 & a^{-n} \end{bmatrix}$$ which means that $(1)$ holds for negative exponents as well. As a consequence, $(2)$ can be generalized to functions admitting a Laurent series representation: $$f(z) = \sum_{n=-\infty}^\infty c_n z^n$$

• Thank you for this wonderful solution! This makes me very curious about generalizing this. Does this mean that if we have an operator $F$ on the space of function which admit Laurent series representations, which satisfies $F(af(x)+bg(x))=aF(f(x))+bF(g(x))$ for scalars $a$ and $b$ and also that $F(x^n)=nx^{n-1}$ for all integers $n$, must it be true that $F$ is the differentiation operator? It seems to me that the only properties that you use are linearity and its effect on $x^n$, so I am curious if this generalizes? Nov 23, 2015 at 3:08
• @ASKASK: I am afraid that goes beyond my knowledge of this topic. Perhaps that follows from the solutions given in the other answers here? Otherwise you might consider to ask a new question about this generalization. Nov 23, 2015 at 6:20
• @ASKASK: Up to possibly some technical constraints on the function space you're working on (and I suspect "admitting Laurent series" is sufficient, but there may be other technicalities), yes, a linear operator that acts as differentiation on monomials should necessarily be the differentiation operator. Nov 23, 2015 at 16:16
• In the last lines it should be $(-a^{-2})^n$, not $(-a^{2})^n$, because $a^{-1}=(-a^{-2})(-a)$. Also in the next line it should be $(-1)^n a^{-2n}$, not $(-1)^n a^{2n}$. Everything else stays the same. Oct 5, 2017 at 17:12
• @user236182: You are right, thank you! Oct 5, 2017 at 18:40

It's a general statement if $$J_{k}$$ is a Jordan block and $$f$$ a function matrix then $$$$f(J)=\left(\begin{array}{ccccc} f(\lambda_{0}) & \frac{f'(\lambda_{0})}{1!} & \frac{f''(\lambda_{0})}{2!} & \ldots & \frac{f^{(n-1)}(\lambda_{0})}{(n-1)!}\\ 0 & f(\lambda_{0}) & \frac{f'(\lambda_{0})}{1!} & & \vdots\\ 0 & 0 & f(\lambda_{0}) & \ddots & \frac{f''(\lambda_{0})}{2!}\\ \vdots & \vdots & \vdots & \ddots & \frac{f'(\lambda_{0})}{1!}\\ 0 & 0 & 0 & \ldots & f(\lambda_{0}) \end{array}\right)$$$$ where $$$$J=\left(\begin{array}{ccccc} \lambda_{0} & 1 & 0 & 0\\ 0 & \lambda_{0} & 1& 0\\ 0 & 0 & \ddots & 1\\ 0 & 0 & 0 & \lambda_{0} \end{array}\right)$$$$ This statement can be demonstrated in various ways (none of them short), but it's a quite known formula. I think you can find it in various books, like in Horn and Johnson's Matrix Analysis.

• Does this have a name? What's the intuition behind it? Nov 22, 2015 at 22:52
• @nbubis is a Jordan block and f a function matrix ? Nov 22, 2015 at 23:42
• In fact, this is sometimes taken to be the definition of a matrix function when evaluated at a Jordan block; see this. See this paper as well. Nov 23, 2015 at 5:23
• Could you include the definition or a link to the definition of a Jordan block in this answer? Nov 23, 2015 at 7:46
• A previous edit changed $J_k$ to $J$ in all but one occurrence. However, simply changing $J_k$ to $J$ in the first sentence doesn't seem to me to read well. Would it be correct to move the given $J$ up to the first line as "... if $J$ is the Jordan block ..."? Apr 5, 2018 at 10:10

$$A =a \mathbb{I}+M$$ where $$M = \begin{bmatrix} 0 & 1 \\ 0 & 0 \end{bmatrix}$$

most relevantly, $M$ is an upper triangular matrix satisfying $M^n=0;n>1$

$$A^n=\left(a \mathbb{I}+M \right)^n$$ Since all these matrices ( $\mathbb{I}$ and $M$ ) commute we can write out the binomial formula ... $$A^n = \sum_{i=0}^n\binom ni a^i \mathbb{I}^iM^{n-i}$$ The only non-zero terms will be $i=n$ and $i=n-1$ $$A^n= a^n \mathbb{I}+na^{n-1}M$$

Here's one for the algebra lovers. It can probably be improved substantially, since I'm not an algebra lover myself. For simplicity, I'll use rational functions with rational coefficients; sophisticated readers may substitute their favorite coefficient fields.

A rational function in the variable $x$ is an element of the field $\mathbb{Q}(x)$. You can think of $\mathbb{Q}(x)$ as a vector space over itself. Multiplication by $x$ is a $\mathbb{Q}(x)$-linear map from $\mathbb{Q}(x)$ to itself, which can be written as the $1 \times 1$ matrix $$X_0 = \left[ \begin{array}{c} x \end{array} \right].$$ The derivative, sadly, is not a $\mathbb{Q}(x)$-linear map. However, we can represent it with a $\mathbb{Q}(x)$-linear map by resorting to trickery. If we happen to know the derivative $f'$ of a function $f$, we can bundle the two together into an element of $\mathbb{Q}(x)^2$: $$\left[ \begin{array}{c} f' \\ f \end{array} \right].$$ Multiplication by $x$ sends this "derivative pair" to $$\left[ \begin{array}{c} (xf)' \\ xf \end{array} \right] = \left[ \begin{array}{c} xf' + f \\ xf \end{array} \right] = \left[ \begin{array}{cc} x & 1 \\ 0 & x \end{array} \right] \left[ \begin{array}{c} f' \\ f \end{array} \right].$$ In other words, multiplication by $x$ acts on derivative pairs in $\mathbb{Q}(x)^2$ by the matrix $$X_1 = \left[ \begin{array}{cc} x & 1 \\ 0 & x \end{array} \right].$$

The set of derivative pairs isn't very nice. In particular, it's not a subspace of $\mathbb{Q}(x)^2$. We can say one thing about it, though: we know it contains $$\left[ \begin{array}{c} 1' \\ 1 \end{array} \right] = \left[ \begin{array}{c} 0 \\ 1 \end{array} \right].$$ Using the matrix $X_1$, we can develop this fact into a complete understanding of derivative pairs.

To see how, recall that the multiplication-by-$x$ operator on $\mathbb{Q}(x)$ is invertible. In fact, plugging the multiplication-by-$x$ operator into any rational function gives a well-defined $\mathbb{Q}(x)$-linear operator on $\mathbb{Q}(x)$. Every rational function can be built up from $1 \in \mathbb{Q}(x)$ by applying a rational function of the multiplication-by-$x$ operator. In matrices, $$\left[ \begin{array}{c} f \end{array} \right] = f(X_0) \left[ \begin{array}{c} 1 \end{array} \right].$$ Similarly, any derivative pair in $\mathbb{Q}(x)^2$ can be built up from $$\left[ \begin{array}{c} 0 \\ 1 \end{array} \right] \in \mathbb{Q}(x)^2$$ by applying a rational function of $X_1$: $$\left[ \begin{array}{c} f' \\ f \end{array} \right] = f(X_1) \left[ \begin{array}{c} 0 \\ 1 \end{array} \right].$$ That explains the mystifying behavior you noticed.

This idea can be extended to higher derivatives. For example, multiplication by $x$ acts on "derivative quadruples" $$\left[ \begin{array}{c} f''' \\ f'' \\ f' \\ f \end{array} \right] \in \mathbb{Q}(x)^4$$ by the matrix $$X_4 = \left[ \begin{array}{cc} x & 3 & 0 & 0 \\ 0 & x & 2 & 0 \\ 0 & 0 & x & 1 \\ 0 & 0 & 0 & x \end{array} \right],$$ yielding the formula $$\left[ \begin{array}{c} f''' \\ f'' \\ f' \\ f \end{array} \right] = f(X_4) \left[ \begin{array}{c} 0 \\ 0 \\ 0 \\ 1 \end{array} \right].$$ I'm certain this whole business is related to Myself's comment about dual numbers, but I haven't worked out how.

Just to expand on the comment of @Myself above...

# Adjoining algebraics as matrix maths

Sometimes in mathematics or computation you can get away with adjoining an algebraic number $\alpha$ to some simpler ring $R$ of numbers like the integers or rationals $\mathbb Q$, and these characterize all of your solutions. If this number obeys the algebraic equation $$\alpha^n = \sum_{k=0}^{n-1} q_k \alpha^k$$ for all $q_k \in R,$ we call the above polynomial equation $Q(\alpha) = 0$, and then we can adjoin this number by using polynomials of degree $n - 1$ with coefficients from $R$ and evaluated at $\alpha$: the ring is formally denoted $R[\alpha]/Q(\alpha),$ "the quotient group of the polynomials with coefficients in $R$ of some parameter $\alpha$ given their equivalence modulo polynomial division by $Q(\alpha).$"

If you write down the action of the multiplication $(\alpha\cdot)$ on the vector in $R^n$ corresponding to such a polynomial in this ring, it will look like the matrix $$\alpha \leftrightarrow A = \begin{bmatrix}0 & 0 & 0 & \dots & 0 & q_0\\ 1 & 0 & 0 & \dots & 0 & q_1 \\ 0 & 1 & 0 & \dots & 0 & q_2 \\ \dots & \dots & \dots & \dots & \dots & \dots \\ 0 & 0 & 0 & \dots & 0 & q_{n-2} \\ 0 & 0 & 0 & \dots & 1 & q_{n-1} \\ \end{bmatrix},$$ and putting such a matrix in row-reduced echelon form is actually so simple that we can immediately strengthen our claim to say that the above ring $R[\alpha]/Q(\alpha)$ is a field when $R$ is a field and $q_0 \ne 0.$ The matrix $\sum_k p_k A^k$ is then a matrix representation of the polynomial $\sum_k p_k \alpha^k$ which implements all of the required operations as matrix operations.