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Fundamentals of Differential Equations, Ch 9.5

I am trying to find a generalized eigenvector in this problem. (I understand the general theory goes much deeper, but we are only responsible for a limited number of cases.)

I have found eigenvectors $\vec {u_1}$ and $\vec {u_2}.$

When I try $u_1$ and $u_2$ as $u_3$ into this equation: $$ (A - I)u_4 = u_3$$ I get systems which are inconsistent.

How can I find the $u_3$? I've been told it has something to do with $(A - I)^3 = 0$, but that's about it.

jaynp
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  • What is the prime doing in the "equation" $\bf x'=Ax$? What is $t$? There is context missing here, impossible to make much sense out of this. – Marc van Leeuwen May 14 '13 at 08:29

1 Answers1

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We are given the matrix:

$$\begin{bmatrix}2 & 1 & 1\\1 & 2 & 1\\-2 & -2 & -1\\\end{bmatrix}$$

We want to find the characteristic polynomial and eigenvalues by solving

$$|A -\lambda I| = 0 \rightarrow -\lambda^3+3 \lambda^2-3 \lambda+1 = -(\lambda-1)^3 = 0$$

This yields a single eigenvalue, $\lambda = 1$, with an algebraic multiplicity of $3$.

If we try and find eigenvectors, we setup and solve:

$$[A - \lambda I]v_i = 0$$

In this case, after row-reduced-echelon-form, we have:

$$\begin{bmatrix}1 & 1 & 1\\0 & 0 & 0\\0 & 0 & 0\\\end{bmatrix}v_i = 0$$

This leads to the two eigenvectors as he shows, but the problem is that we cannot use that to find the third as we get degenerate results, like you showed.

Instead, let's use the top-down chaining method to find three linearly independent generalized eigenvectors.

Since the RREF of

$$[A - 1 I] = \begin{bmatrix}1 & 1 & 1\\0 & 0 & 0\\0 & 0 & 0\\\end{bmatrix}$$

We have $E_3 = kernel(A - 1I)$ with dimension $= 2$, so there will be two chains.

Next, since

$$[A - 1 I]^2 = \begin{bmatrix}0 & 0 & 0\\0 & 0 & 0\\0 & 0 & 0\\\end{bmatrix}$$

the space Kernel $(A-1I)^2$ has dimension $=3$, which matches the algebraic multiplicity of $\lambda=1$.

Thus, one of the chains will have length $2$, so the other must have length $1$.

We now form a chain of $2$ generalized eigenvectors by choosing $v_2$ in kernel $(A-1I)^2$ such that $v_2$ is not in the kernel $(A-1I)$.

Since every vector is in kernel $(A-1I)^2$, and the third column of $(A-1I)$ is non-zero, we may choose:

$$v_2 = (1, 0, 0) \implies v_1 = (A-1I)v_2 = (1,1,-2)$$

To form a basis for $\mathbb R^3$, we need one additional chain of one generalized eigenvector. This vector must be an eigenvector that is independent from $v_1$. Since

$$E_3 = ~\text{span}~ \left(\begin{bmatrix}0\\1\\-1\\\end{bmatrix}, \begin{bmatrix}-1\\0\\1\\\end{bmatrix}\right).$$

and neither of these spanning vectors is itself a scalar multiple of $v1$, we may choose either one of them. So let

$$w_1 = (0, 1, -1).$$

Now we have two chains:

$$v_2 \rightarrow v_1 \rightarrow 0$$

$$w_1 \rightarrow 0$$

So, to write the solution, we have:

$\displaystyle 1^{st}$ Chain

$$x_1(t) = e^t \begin{bmatrix}1\\1\\-2\\\end{bmatrix}$$

$$x_2(t) = e^t\left(t \begin{bmatrix}1\\1\\-2\\\end{bmatrix} + \begin{bmatrix}1\\0\\0\\\end{bmatrix}\right)$$

$\displaystyle 2^{nd}$ Chain

$$x_3(t) = e^t \begin{bmatrix}0\\1\\-1\\\end{bmatrix}$$

Thus:

$$x(t) = x_1(t) + x_2(t) + x_3(t)$$

Note, you can use this linear combination of $x(t)$ and verify that indeed it is a solution to $x' = Ax$.

Amzoti
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  • Nice work!! (as usual!), and accepted, so I'm sure it "took". congrats on a job well done! – amWhy May 15 '13 at 02:50
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    You too! Enjoy work (presentation) and relaxation! ;-) – amWhy May 15 '13 at 03:17
  • @Amzoti If $w_1$ is to be an eigenvector, shouldn't it be $\text{col} (-1, 1, 0)$ instead of $\text{col} (0, 1, -1)$ – jaynp May 15 '13 at 03:35
  • @user1850672: We could have chosen either ot the two in the span. I chose the first one. There may be other choices too. regards – Amzoti May 15 '13 at 03:37
  • @Azmoti No no, I see that you could choose either in $E_3$'s span. Look at the eigenvectors given in the problem. The first is different from your first. Is this correct? Forgive me if I'm misunderstanding something. – jaynp May 15 '13 at 04:33
  • @user1850672: yes, the two original eigenvectors are from the beginning of the problem, but I abandoned those due to having to resort to another method. the original method (as I eluded to) are exactly what the author stated, but those did not lead us to finding three independent ones. I used this other method to derive three other linearly independent eigenvectors. Sorry if that wasn't clear. Regards – Amzoti May 15 '13 at 04:37
  • @Amzoti I am understanding a little better now, but how exactly was this particular spanning set for $E_3$ determined in this line: $E_3 = ~\text{span}~ \left(\begin{bmatrix}0\1\-1\\end{bmatrix}, \begin{bmatrix}-1\0\1\\end{bmatrix}\right).$ Thanks! – jaynp May 15 '13 at 05:12
  • You need to go back and look up a spanning set for $\mathbb R^3$. This problem really tests linear algebra skills. Just look up spanning set since you are looking for a pair of independent vectors that span. You might also want to search for this method on the web to see other examples. – Amzoti May 15 '13 at 05:16