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I generate a random matrix. It has this general form:

$$\mathbf{B}=\left[ \matrix{ \mathbf{A}_1&\mathbf{A}_2&\ldots&\mathbf{A}_{p-1}&\mathbf{A}_p \\ \mathbf{I}_M&\mathbf{I}_M&\ldots&\mathbf{I}_M&\mathbf{O}_M \\ \vdots &\vdots &\ddots& \vdots& \vdots\\ \mathbf{I}_M&\mathbf{I}_M&\ldots&\mathbf{I}_M&\mathbf{O}_M \\ } \right]:(pM \times pM)$$

in which $\mathbf{A}_i$ is $M \times M$ for $i=1,\ldots,p$ and $\mathbf{O}_M$ ($M\times M$) and $\mathbf{I}_M$ ($M\times M$) are zero and identity matrices.

The elements of $\mathbf{A}_i$ are random (they are from a multivariate normal distribution). The eigenvalues of $\mathbf{B}$ should be less than one and I don't want to repeat random number generation process until this happens. I want to change some elements of a generated matrix (whose at least one of its eigenvalues is larger than one) so that all eigenvalues become less than one. Is there any way to do so?

(I think I should answer this question: If I change the $B(i,j)$, how will eigenvalues of $B$ be affected? But I don't know the answer).

edit: I have this idea: What if I generate a set of random eigenvalues and then generate my matrix based on them. I don't know how to proceed.

Thanks.

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I assume you mean eigenvalues in the range $[0,1]$, otherwise $-B$ would do in case all eigenvalues of $B$ are positive... –  draks ... Nov 28 '12 at 8:12
    
in fact the eigenvalues in the the unit circle, since there is the possibility of complex numbers. –  Ramin Nov 28 '12 at 8:15
2  
The pattern isn't quite clear -- are all the lower matrices $\mathbf I_M$ except $\mathbf O_M$ in the last column? Also, I think you need to say something about the type of modification you'd like -- in what sense should the resulting matrix be close to the original randomly generated $\mathbf B$? One option would be to just scale $\mathbf B$ down. –  joriki Nov 28 '12 at 9:05
    
@joriki: Thanks. I edited the pattern. You are right; If I define $\mathbf{C=B}/10$, then its eigenvalues will be $1/10$-th of $\mathbf{B}$. But is there a way to change the minimum number of elements? –  Ramin Nov 28 '12 at 9:32
    
@Ron: I'd be surprised if it's tractable to find the minimal number of elements to change. –  joriki Nov 28 '12 at 9:36
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1 Answer 1

I don't have an answer to your question, but here are some thoughts.

If your normal distribution is able to generate a $B$ whose spectral radius is $\ge1$, then any procedure to modify $B$ to make its spectral radius less than one would clearly result in non-conformance to the normal distribution. So, I assume that you want to modify $B$ such that it follows a conditional distribution of normal distribution.

This seems to me a difficult problem, but the difficulty goes beyond that. In fact, when $p$ and $M$ are large, any such procedure would necessarily mean that you are drawing samples from the tails of normal distribution, i.e. you are mostly using extreme events as samples.

Here is an explanation. Let $v=(v_1^T,v_2^T,\ldots,v_n^T)^T$ be an eigenvector of $B$. If $v$ corresponds to the zero eigenvalue, then the equation $Bv=0$ means $$ \begin{pmatrix} A_1 & A_2 & \ldots & A_{p-1} & A_p\\ I & I & \ldots & I & 0 \end{pmatrix}v=0 $$ The rank of the matrix on the LHS is almost surely $2M$, so its null space is $(p-2)M$-dimensional and $B$ almost surely has $2M$ nonzero eigenvalues.

Now, suppose $\lambda$ is a nonzero eigenvalue of $B$. From $Bv=\lambda v$, we get \begin{align} A_1v_1 + \sum_{i=2}^p A_pv_p &= \lambda v_1,\\ v_1 + v_2+v_3+\ldots+v_{p-1} &= \lambda v_2,\\ v_1 + v_2+v_3+\ldots+v_{p-1} &= \lambda v_3,\\ &\vdots\\ v_1 + v_2+v_3+\ldots+v_{p-1} &= \lambda v_p. \end{align} In other words, $v_2=v_3=\ldots=v_p=$ some vector $u$, $v_1 = [\lambda-(p-2)]u$ and \begin{align} A_1[\lambda-(p-2)]u + \sum_{i=2}^p A_pu = \lambda [\lambda-(p-2)]u,\\ \textrm{i.e. }\quad C_\lambda u:=\left(\lambda^2 I - \lambda [(p-2) I + A_1] + \left[(p-2)A_1 - \sum_{i=2}^p A_p\right]\right)u = 0. \end{align} Let $q(\lambda) = \det\,C_\lambda$. Then $q(\lambda)=0$ is a degree-$2M$ polynomial equation in $\lambda$. The sum $S$ of its $2M$ roots is the coefficient of $\lambda^{2M-1}$ in $q(\lambda)$, i.e. $S = \mathrm{trace}\left((p-2) I + A_1\right) = M(p-2)+\mathrm{trace}\,A_1$. In order that these $2M$ roots all have moduli smaller than $1$, $|S|$ must be smaller than $2M$. Hence $\left|(p-2)+\frac{\mathrm{trace}\,A_1}{M}\right|<2$. When $M$ is large, however $\frac{\mathrm{trace}\,A_1}{M}$ will be distributed around the mean $\mu$ of the normal distribution, with variance of order $O(1/M)$. Since $\left|(p-2)+\mu\right|\ge2$ when $p$ is large, we conclude that, when both $M$ and $p$ are large, the condition $|S|<2$ is satisfied only when $\frac{\mathrm{trace}\,A_1}{M}$ is an extreme value.

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thanks a lot. I think I should have mentioned that the matrix is square. –  Ramin Nov 28 '12 at 15:38
    
@Ron: What do you mean? I didn't say that $B$ is not square. I thought it was square all along. –  user1551 Nov 28 '12 at 16:08
    
Sorry, my bad. I'm working on it. thanks –  Ramin Nov 28 '12 at 16:34
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