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Does there exist a definition for matrix exponentiation? If we have, say, an integer, one can define $A^B$ as follows:

$$\prod_{n = 1}^B A$$

We can define exponentials of fractions as a power of a radical, and we even have the following definition of the exponential:

$$e^z = \sum_{n = 0}^\infty \frac{z^n}{n!}$$

which comes from a taylor series for the function $\exp(z)$. Now, a problem seems to arise when we attempt to calculate $\exp(A)$, where $A$ is an $n$ x $n$ (square) matrix. We cannot define it as multiplication a "matrix number of times" as this makes no sense. The only reasonable definition that could work is the latter definition (the infinite series):

$$e^A = 1 + A + \frac{AA}{2!} + \frac{AAA}{3!} + \frac{AAAA}{4!} + \cdots$$

where we can define matrix exponentiation to the power of an integer, which is all that is required here. We know that $e^x$ will converge absolutely for all complex numbers, but do we know that this is true for matrices? Can this "matrix sum" diverge, and are there ways to test divergence/convergence when a matrix is applied? Or is this concept of "matrix divergence" not well defined?

Thanks.

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  • $\begingroup$ This exponentiation appears in the Lie theory. If $ A $ is a lie algebra, then the exponentiation provides the local structure of the corresponding Lie group. In the case of Lie theory, at least, we can say that this exponentiation converges. $\endgroup$ – user122283 Jan 20 '14 at 15:45
  • $\begingroup$ Isn't a matrix a higher dimensional complex number? If so, then couldn't we apply the same divergence or convergence rules to this exponentiation also? $\endgroup$ – user122283 Jan 20 '14 at 15:46
  • $\begingroup$ I'd suggest editing the title, as you're only really asking about $e^A$, rather than $x^A$ for a given $x\in\Bbb R$. $\endgroup$ – Jonathan Y. Jan 20 '14 at 15:59
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Yes, the exponential of a matrix can be defined by that Taylor series, and it is a very useful thing. See e.g. Wikipedia The proof that it converges is not difficult, using any sub-multiplicative matrix norm. If $\| \cdot \|$ is such a norm, then any power series $\sum_n c_n A^n$ converges whenever the real series $\sum_n |c_n| \|A\|^n$ converges.

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My favourite definition - as I teach quite often ODEs - is using differential equations.

Let us first note that $x(t)=\mathrm{e}^{at}$ is the unique solution of the initial value problem: $$ x'=ax, \quad x(0)=1. $$ Generalise that a little bit: Let $A$ be a square matrix ($A\in\mathbb R^{n\times n}$). Define as $\mathrm{e}^{tA}$ the unique, and globally defined, solution of $$ X'=AX, \quad X(0)=I, $$ where $X$ is an $n\times n$ matrix, and $I$ the identity matrix. Through this definition a lot of properties of the exponential of a matrix, (which in order to prove require messy calculations) are proved very elegantly if we use the above definition.

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For diagonalizable $A$, you can write $A = P \Lambda P^{-1}$, where the column vectors of $P$ are eigenvectors and $\Lambda$ is diagonal matrix with eigenvalue entries.

Then $$e^{A} = e^{P \Lambda P^{-1}} = PIP^{-1} + P \Lambda P^{-1} + \frac{1}{2!}(P \Lambda P^{-1})^2 + \ldots = Pe^{\Lambda}P^{-1}$$

And raising $e$ to a diagonal matrix is easy—just replace all the diagonal entries with those raised by $e$.

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