3

Let $M_n$ be a sequence of matrices that converges to $M$, component-wise: $(M_n)_{ij}\to M_{ij}$ as $n\to\infty$ for each pair $i,j$. Suppose that $\lambda_n,v_n$ is an eigenpair for $M_n$ each $n$, and that $\lambda,v$ is an eigenpair for $M$.

A general question of interest is: when does $M_n\to M$ and $\lambda_n\to \lambda$ imply that $v_n\to v$?

My specific situation is that: all matrices are nonnegative (each matrix component is nonnegative), irreducible (strongly connected directed graph), aperiodic (gcd 1 for all directed paths $i$ to $j$) and hence primitive (unique max mod eigenvalue) and that the eigenpairs are all Perron eigenpairs. (See below for additional background.)

Now suppose that $\lambda_n,v_n$ is the Perron eigenpair for $M_n$ and similarly for $\lambda,v$. Assume that $M_n\to M$ and $\lambda_n\to \lambda$ as $n\to\infty$. Is it possible to prove that $v_n\to v$? Edit: I also meant to mention that each eigenvector sums to one, i.e. $\sum_j v_j=1$.

I don't write all of my own attempts here, but they are all variations on basic analysis methods, triangle inequalities, $\epsilon/3$ stuff etc. I though maybe taking a subsequence: since $(v_n)_j$ (the sequence for the $j^{th}$ component of $v_n$) is a bounded sequence, it has a convergent subsequence. But the subsequence of indices could be different for each $j$.

Maybe someone can come up with a counterexample?

Additional background:

"Irreducible" means that if you draw a directed edge between indexes $i\to j$ when $M_{ij}>0$, and $i\leftarrow j$ when $M_{ji}>0$, and that if we draw that directed graph for the entire matrix we can find a directed path from each index to every other index.

This graph is called "aperiodic" if the greatest common divisor for the number of steps for all possible paths between indices $i$ and $j$ is one---i.e. that you don't have a situation where any path from $i$ to $j$ is, say, always a multiple of $2$ (giving period 2).

"Primitive" means that there is a unique eigenvalue of maximum modulus which is algebraically simple, i.e. every other eigenvalue will have a strictly less absolute value. We call the unique max mod eigenvalue and its eigenvector the Perron eigenpair. This vector is also strictly positive.

===============================

EDIT: (solution attempt)

We have $M_nv_n=\lambda_nv_n$ for each $n$, $\lambda_n\to\lambda$, and that $M_n\to M$ and that $v$ is the only eigenvector (up to scalar multiple) for eigenvalue $\lambda$. I am also assuming that the eigenvectors are strictly positive and that the components of each $v_n$ and $v$ sum to one, e.g. $\sum_j v_j=1$.

We have: $$\begin{aligned} (\lambda I-M)v_n&=(\lambda_n I-M - \lambda_n I +\lambda I)v_n\\ &=(M_n I-M)v_n - (\lambda_n -\lambda)v_n \end{aligned}$$ Hence taking the $j^{th}$ component: $$\left|[(\lambda I-M)v_n]_j\right| \leq \left|[(M_n-M)v_n]_j\right| + \left| \lambda_n-\lambda\right| |[v_n]_j|.$$

Now we can choose $N$ so large such that for any $n>N$ we have each term on the right arbitrarily small. Thus the left side can be made arbitrarily small for all $j$ simultaneously (note that $v_n$ has bounded components since they are "normalized" to sum to one and nonnegative). This means that $(\lambda I-M)v_n$ can be made arbitrarily close to the zero vector. This is only possible if $v_n$ gets arbitrarily close to $v$, since $(\lambda I-M)x$ can only be close to zero if $x$ is close to $v$ (because $v$ is the only eigenvector that goes with eigenvalue $\lambda$ for $M$--- the only one whose components sum to one that is).

jdods
  • 6,248

2 Answers2

4

More generally, suppose $M$ is a matrix with a simple eigenvalue $\lambda$ (i.e. generalized eigenspace one-dimensional) and $M_n$ a sequence of matrices converging to $M$. The eigenprojection for $M$ on $\lambda$ is $P(M) = \frac{1}{2\pi i} \oint_C (zI - M)^{-1}\; dz$, where $C$ is a positively oriented closed contour with $\lambda$ inside and all other eigenvalues for $M$ outside. If $n$ is sufficiently large, $P(M_n) = \frac{1}{2\pi i} \oint_C (zI-M_n)^{-1}\; dz$ is also a one-dimensional projection, and $P(M_n) \to P(M)$ as $n \to \infty$. In fact, $P(M_n)$ is the eigenprojection for a simple eigenvalue $\lambda_n$ of $M_n$ with $\lambda_n \to \lambda$ as $n \to \infty$, and the corresponding eigenvector $v_n$ (which we can take as $P(M_n) v$ where $v$ is an eigenvector of $M$ for $\lambda$) converges to $v$ as $n \to \infty$.

Robert Israel
  • 448,999
3

The unit sphere of an Euclidean space is compact so there is a subsequence $(v_{n_i})$ converging to some vector $w$ of norm $1$. Taking limits in the equation $M_{n_i}v_{n_i}=\lambda_{n_i}v_{n_i}$ we get $Mw=\lambda w$. Since the eigen space coresponding to $\lambda $ is one dimensional we get $w=v$.

Now use the fact that $v_n \to v$ iff every subsequence of $(v_n)$ has a further subsquence which converges to $v$.

geetha290krm
  • 36,632
  • 1
    What does $\lambda = 1$ have to do with it? When the matrix is nonnegative, irreducible and aperiodic, the Perron eigenspace is one-dimensional. – Robert Israel Feb 07 '23 at 23:59
  • I meant for the eigenvectors to sum to one. I think I forgot to mention that. Does that work or do we really need to normalize in the standard way? I'm working with strictly positive eigenvectors (probably just nonnegative is ok), and strictly positive eigenvalues (actually all are less than one, but not sure that matters here). – jdods Feb 08 '23 at 00:00
  • @RobertIsrael I misread the question. I have edited. – geetha290krm Feb 08 '23 at 00:07
  • I'm sure you are correct, but I just don't see why... why does the $v_n$ have a subsequence where each component converges simultaneously? Why can't $v_n$ just oscillate around the unit sphere? If the eigenspace for $\lambda$ wasn't 1D, would we still be guaranteed convergence, but just to some vector in that eigenspace? – jdods Feb 08 '23 at 00:11
  • 1
    Compactness implies convergence of a subsequence. All coordinates will converge for a suitable subsequence. @jdods – geetha290krm Feb 08 '23 at 00:19
  • Ah... right, the idea of compactness just extends to general spaces of any dimension... The sequence of vectors itself is a compact set and hence has a convergent subsequence. And of course the limit must be in the eigenspace of $\lambda$ for $M$. Ok. And if that eigenspace is not 1D, then there might not be convergence at all, since different subsequences could converge to different vectors in that eigenspace. Thank you so much for clearing this up! – jdods Feb 08 '23 at 00:27
  • 1
    This shows that there is a subsequence converging but not that the original sequence converges. To finish, you could note that by the same argument, every subsequence has a sub-subsequence converging to $w$ and apply this fact. – Jair Taylor Feb 08 '23 at 00:36
  • @JairTaylor I think you are right, because there could be subsequences that don't converge and I'm not certain how to argue otherwise... – jdods Feb 08 '23 at 02:02
  • 1
    @jdods That is what I had in mind, but I didn't bother to state that explicitly. I have now added a paragraph to my answer. – geetha290krm Feb 08 '23 at 04:48
  • This answer uses methods I understand. Thanks! Just posting the link for reference: https://en.wikipedia.org/wiki/Bolzano%E2%80%93Weierstrass_theorem#Sequential_compactness_in_Euclidean_spaces

    I was working with a segment of a plane in $\mathbb R^n$ which is still a compact set (as long as I close the edges and allow points to have the coordinate zero), the set of all points with nonnegative coordinates that sum to one. That's a sequentially compact set, so my $v_n$ must converge to something in that set.

    – jdods Feb 08 '23 at 13:38