Radius of Convergence of a Matrix Power Series

calculuslinear algebrapower series

I am trying to figure out the conditions for a matrix, for the analytic matrix function to converge. For example,

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

converges for every $A \in M_{n} (\mathbb{C})$. However, some series might diverge for some matrices in $A \in M_{n} (\mathbb{C})$ (which I can't come up with an example right now).

I've looked up Wikipedia and it says :

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in this article, which basically means that every analytic function of a matrix can be evaluated as a finite-degree polynomial of the matrix. (Hence, it converges.) However, in this article, it says :

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which clearly states the condition for the power series to converge. Also, in this article that is not that trustable (sorry, it's in Korean), it says that the power series converges only if every eigenvalue of $A$ is in the radius of convergence of the original power series. (In the complex plane if necessary.)

What is the correct answer? What is the 'radius of convergence' of a matrix power series? I'm very confused right now.

Best Answer

You didn't understand what the Wikipedia article says. It says the IF the series $f(X)$ converges, THEN the limit is a polynomial in $X$. It doesn't always converge. For example, take $f(X) = \sum_{k \geqslant 0} X^k$ and $X = \lambda I_n$ a homethety, then $f(X) = \left(\sum_{k \geqslant 0} \lambda^k\right)I_n$ converges if and only if $|\lambda| < 1$.

What says you Korean article is almost right however. Let $(a_n)$ be a sequence of complex numbers and $R$ be its convergence radius. Let $X$ be an $n \times n$ matrix with complex coefficients and $\lambda_1,\ldots,\lambda_n$ be its eigenvalues counted with multiplicities and $\rho = \max\{|\lambda_k|\}$ be its spectral radius. You can prove that $\rho < R \Rightarrow f(X)$ converges $\Rightarrow \rho \leqslant R$ and the case $\rho = R$ is complicated, just like for classical complex series.

You can prove the second statement by contraposition. Let $v_i$ be a non zero vector such that $Xv_i = \lambda_iv_i$ with $|\lambda_i| > R$. Then $f(\lambda_i)$ diverges hence $$ \left(\sum_{k = 0}^K a_kX^k\right)v_i = \left(\sum_{k = 0}^K a_k\lambda_i^k\right)v_i \textrm{ diverges when $K \rightarrow +\infty$}, $$ so $f(X)$ diverges.

For the first statement, begin by the simple case where $X = P^{-1}DP$ is diagonalisable. We have $D = \mathrm{diag}(\lambda_1,\ldots,\lambda_n)$ and, \begin{align*} \sum_{k = 0}^K a_kX^k & = \sum_{k = 0}^K a_kPD^kP^{-1}\\ & = P\left(\sum_{k = 0}^K a_k\mathrm{diag}(\lambda_1^k,\ldots,\lambda_n^k)\right)P^{-1}\\ & = P\mathrm{diag}\left(\sum_{k = 0}^K a_k\lambda_1^k,\ldots,\sum_{k = 0}^K a_k\lambda_n^k\right)P^{-1}\\ & \underset{K \rightarrow +\infty}{\longrightarrow} P\mathrm{diag}(f(\lambda_1),\ldots,f(\lambda_n))P, \end{align*} and this limit exists because each $\lambda_i$ verifies $|\lambda_i| \leqslant \rho < R$.

Now, in the general case, write $X = Y + N$ using Jordan-Chevalley decomposition ($Y$ is diagonalisable with the same eigenvalues as $X$ and $N$ is nilpotent, $Y$ and $N$ commute). $N$ is nilpotent thus $N^n = 0$ and the commutativity implies that we can use Newton binom formula to get that for all $k$, $$ X^k = \sum_{i = 0}^k \binom{k}{i}N^iY^{k - i} = \sum_{i = 0}^n \binom{k}{i}N^iY^{k - i}, $$ with the convention $\binom{k}{i} = 0$ when $i > k$. Therefore, for all $K \geqslant n$, \begin{align*} \sum_{k = 0}^K a_kX^k & = \sum_{k = 0}^K a_k\sum_{i = 0}^n \binom{k}{i}N^iY^{k - i}\\ & = \sum_{i = 0}^n N^i\sum_{k = i}^K a_k\binom{k}{i}Y^{k - i}\\ & = \sum_{i = 0}^n N^i\sum_{k = 0}^{K - i} a_{k + i}\binom{k + i}{i}Y^k \end{align*} Now, let for all $i$, $f_i(x) = \sum_{k \geqslant 0} a_{k + i}\binom{k + i}{i}x^k$. $k \mapsto \binom{k + i}{i}$ is a polynomial in $k$ of degree $i$ hence it doesn't affect the radius of convergence of $f_i$, which equals $R$. By the first case ($Y$ is diagonalisable), $f_i(Y)$ exists and when $K \rightarrow +\infty$ in the previous equality, we obtain that $f(X)$ exists with, $$ f(X) = \sum_{i = 0}^n N^if_i(Y) = \sum_{i = 0}^n N^i\sum_{k \geqslant 0} a_{k + i}\binom{k + i}{i}Y^k. $$ It proves that $f$ is well defined on the open set $\{X \in \mathrm{Mat}_n(\mathbb{C})|\rho(X) < R\}$ and we probably have uniform convergence on all compact subsets, just like complex series.

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