# A Neat Bound on the Matrix Exponential

*Pre-requisites:* Linear algebra, ODEs

## Background

The exponential is one of the most fundamental functions in mathematics, and especially in differential equations. Using Taylor series, it is possible to prove that

for any $x \in \mathbb{R}$. This sum is absolutely convergent for every $x$, so it also makes sense even in situations where $x$ is not a real number. This is, for instance, how $e^z$ is defined when $z$ is a complex number, giving rise to the meme-worthy formula

This sum is also how you define the exponential of a (square) matrix.

## The Matrix Exponential

The exponential of a real number shows up literally everywhere when studying differential equations with a single variable, such as

In the same way, the matrix exponential shows up all over the place when studying systems of differential equations, such as

In particular, the matrix exponential shows up in linear differential equations. Take for instance the following initial value problem $w(t) \in \mathbb{R}^n$, and $A \in M_{n \times n}(\mathbb{R})$.

This has the (unique) solution

This solution can be verified by replacing $e^{A t}$ with its defining Taylor series, and differentiating term by term. Estimates of the matrix exponential help mathematicians to understand the behavior of vector-valued differential equations.

## Preliminary Bounds

My goal in this post is to present a neat bound on the matrix exponential, but first I think it is prudent to go through some alternative bounds on the exponential. Because the following discussion requires us to use eigenvalues and eigenvectors, which may be complex, we naturally will naturally work with matrices in $M_{n \times n}(\mathbb{C})$. For $A \in M_{n \times n}(\mathbb{C})$, let $\|A\|$ denote its *spectral norm*.

Within the complex numbers, exponential is very well-understood. Euler's famous formula gives that:

In particular,

Now, consider exponentials in $M_{n \times n}(\mathbb{C})$. We get the basic bound

Thus,

This bound can be pretty lousy at times, but it is a start. For instance, if $A = -t 1$, where $1$ is the identity matrix, then $\|e^A\| = e^{-t}$. In constrast, the above bound only guarantees that $\|e^A\| \leq e^t$.

For certain matrices, we can compute $e^A$ exactly, and use this to get a much more exact bound. Let $A$ be a diagonal matrix:

Then, the matrix exponential is exactly

and we have the following bound

If $A$ is diagonalizable, then we can get a bound similar to (4). Suppose that $A = T D T^{-1}$, where $D$ is a diagonal matrix. Then,

The diagonal entries of $D$ are exactly the eigenvalues of $A$. Let $\lambda_1 \dots \lambda_n$ be the eigenvalues of $A$, and define

It can be shown that $\Lambda$ is a continuous function $M_{n \times n}(\mathbb{C}) \rightarrow \mathbb{R}$. With this notation, we can write the approximation:

More generally, any square matrix $A$ is similar to a matrix $J$ in *Jordan canonical form*, $A = T J T^{-1}$. The exponential $e^J$ can be computed exactly, yielding the bound:

for some constant $C_n > 0$ that depends only on $n$, the dimension. This bound, although very useful, is not entirely satisfying because it depends on $T$, which may be very hard to compute and may be arbitrarily large.

## A Pretty Good Bound

It turns out that:

The constant $C_n$ may be quite large, but it is at most exponential in $n$. Since $\Lambda$ is a continuous function, this bound is actually continuous in $A$, which is why I like it so much.

For a period of time, I was very interested in understanding the matrix exponential, but I had trouble finding any bounds similar to (7). This is one of the reasons why I'm making this blog post. Eventually, with the help of Math Stack Exchange, I received an answer from user Anton Malyshev. The proof that I present here is a paraphrasing of his proof.

*Proof:* If $n = 0$, then this bound is clearly true. Proceed by induction on $n$.

Let $A \in M_{n \times n}(\mathbb{C})$. Let $v$ be a unit eigenvector of $A$ with eigenvalue $\lambda$. Without loss of generality, suppose that $\Lambda(A) = \Re \lambda$. Let $V$ be the 1-dimensional subspace of $\mathbb{C}^n$ spanned by $v$, and let $W$ be the orthogonal subspace to $V$. Note that $\dim W = n-1$. Find $b \in V$ and $D: V \rightarrow V$ such that, for $w \in W$,

Informally, we could write $A$ in block matrix form as:

To compute $e^A$, consider the initial value problem

The (unique) solution to this differential equation is:

Thus, to bound $e^A$, it suffices to bound $u(1)$.

We next project equation (8) onto $V$ and $W$. Let $u(t) = u_V(t) + u_W(t)$ where $u_V(t) \in V$ and $u_W(t) \in W$. Then, (8) becomes the following:

Now, $u_W(t)$ satisfies a simple linear equation. We can thus solve for $u_W(t)$ exactly.

By induction, we have the bound:

Note that $\|D\| \leq \|A\|$, $\Lambda(D) \leq \Lambda(A)$, and $\|u_W(0)\| \leq \|u_0\|$. This yields the following, somewhat simpler bound:

Bounding $u_V(t)$ is trickier, but possible. Since $u_V(t)$ is a multiple of $V$, we can define $\alpha: \mathbb{R} \rightarrow \mathbb{C}$ by: $u_V(t) = \alpha(t) v$. Plugging this in to (9) gives us the differential equation:

The solution to to this differential equation can be approximated using integrating factors. Define $\beta(t) = e^{-\lambda t} \alpha(t)$. Some routine calculations follow...

Excellent! We can now use the bounds for $u_W(t)$ from equation (10).

This can be integrated by hand, and approximated by:

This completes the proof. $\square$.

## Final Comments

The bound (7) doesn't always give great results, especially if $A$ is close to a diagonal or Hertitian matrix. Bound (7) can be improved to:

or

Here, $A^*$ represents the adjoint of $A$. These bounds can be further refined, but they become more messy.