Linear homogenous system with repeated eigenvalues

  • #1
psie
122
12
Homework Statement
Solve the IVP ##x'=Ax, x(0)=x_0##, where ##A=\begin{pmatrix} 1&1&-1\\ 0&2&-2\\-1&1&-1\end{pmatrix}## and ##x_0=\begin{pmatrix} -1 \\ 1\\ 0\end{pmatrix}##.
Relevant Equations
Determinants, Gaussian elimination, generalized eigenvectors, matrix exponential, etc.
I've solved this problem using a fairly involved technique, where I compute the matrix ##e^{tA}## (the fundamental matrix of the system) with a method derived from the Cayley-Hamilton's theorem. It is a cool method that I believe always works, but it can be a lot of work sometimes. It involves finding a polynomial function, which you then evaluate at the matrix ##A##. Once you've found ##e^{tA}##, the general solution to the system is ##e^{tA}x_0##

Anyway, I've been presented another solution and I'm not really sure what formula they used in the end. To be honest, their whole approach is very new to me and I'd be grateful for any references where this is explained in more detail.

So the eigenvalues of ##A## are ##2## and ##0##, the latter with multiplicity ##2##. The solution goes then as follows:

The generalized eigenvectors for ##\lambda=0## are given by the null space to $$A^2=\begin{pmatrix} 2&2&-2 \\ 2&2&-2\\0&0&0\end{pmatrix}$$ which has a basis ##(0,1,1)## and ##(1,0,1)##. The eigenvalue for ##\lambda=2## are given by the null space to $$A-2I=\begin{pmatrix} -1&1&-1 \\ 0&0&-2\\-1&1&-3\end{pmatrix}$$ which gives the eigenvector ##(1,1,0)##. The general solution is \begin{align} x(t)&=(I+tA)(c_1(0,1,1)+c_2(1,0,1))+c_3e^{2t}(1,1,0) \tag1 \\&=c_1(0,1,1)+c_2((1,0,1)-2t(0,1,1))+c_3e^{2t}(1,1,0) \tag2\end{align}

The initial value ##(-1,1,0)=(0,1,1)-(1,0,1)## is a generalized eigenvector for ##\lambda=0## so we get the solution ##x(t)=(I+tA)(-1,1,0)=(-1,1,0)+t(0,2,2)##.

1. What formula are they using in ##(1)##, i.e. where does the ##(I+tA)## come from?
2. The initial value is a linear combination of generalized eigenvectors. I do not understand how they conclude ##x(t)=(I+tA)(-1,1,0)##?

Grateful if someone could answer both questions and possibly refer to some text where this method is explained in more detail.
 
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  • #2
1) If [itex]A[/itex] has an eigenvalue [itex]\lambda[/itex] with multiplicity 2, then there exist non-zero vectors [itex]u[/itex] and [itex]v[/itex] such that [tex]\begin{split}
(A - \lambda I)v &= u, \\
(A - \lambda I)u &= 0.\end{split}[/tex] It follows that if [itex]x = a(t)u + b(t)v[/itex] and [itex]\dot x = Ax[/itex] then [tex]
\begin{split} \dot a u + \dot b v &= A(au + bv) \\
&= \lambda a u + b (\lambda v + u) \end{split}[/tex] so that [tex]\begin{split}
\dot a &= \lambda a + b \\
\dot b &= \lambda b \end{split}[/tex] and [tex]\begin{split}
x(t) &= a_0 e^{\lambda t} u + b_0 e^{\lambda t} (tu + v) \\
&= a_0 e^{\lambda t} u + b_0 e^{\lambda t} (t(A - \lambda I) + I)v. \end{split}[/tex] Since [itex](A - \lambda I)u = 0[/itex] we can add [itex]a_0e^{\lambda t}t(A - \lambda I)u = 0[/itex] to the right hand side to get [tex]
\begin{split}
x(t) &= a_0 e^{\lambda t} (t(A - \lambda I) + I)u + b_0 e^{\lambda t} (t(A - \lambda I) + I)v \\
&= e^{\lambda t}(t(A - \lambda I) + I)(a_0 u + b_0 v). \end{split}[/tex]

2) You need to solve [tex]
(-1, 1, 0) = x(0) = c_1(0,1,1) + c_2(1,0,1) + c_3 (1,1,0).[/tex] It happens that [itex](c_1, c_2, c_3) = (1, -1, 0)[/itex].
 
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  • #3
A better explanation is that here [itex]\mathbb{R}^3 = \ker A^2 \oplus \ker (A - 2I)[/itex], so that any [itex]x \in \mathbb{R}^3[/itex] can be written uniquely as the sum of a vector [itex]v \in \ker A^2[/itex] and a vector [itex]u \in \ker (A - \lambda I)[/itex].

For [itex]v \in \ker A^2[/itex] we have [itex]A^2 v = 0[/itex] so that [itex]e^{tA}v = (tA + I)v[/itex] and for [itex]u \in \ker (A - 2I)[/itex] we have [itex]Au = 2u[/itex] so that [itex]e^{tA}u = e^{2t}u[/itex]. Hence [tex]\begin{split}
e^{tA}x &= e^{tA}v + e^{tA}u \\
&= (tA + I)v + e^{2t}u.\end{split}[/tex]
 
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1. What is a linear homogeneous system with repeated eigenvalues?

A linear homogeneous system with repeated eigenvalues is a system of linear equations where the eigenvalues of the corresponding matrix are repeated. This means that there are multiple identical eigenvalues for the matrix, which can complicate the process of finding the eigenvectors.

2. How do you solve a linear homogeneous system with repeated eigenvalues?

To solve a linear homogeneous system with repeated eigenvalues, you first find the eigenvalues of the matrix. Then, for each repeated eigenvalue, you find the corresponding eigenvectors by solving the system of equations (A - λI)x = 0, where A is the matrix, λ is the repeated eigenvalue, I is the identity matrix, and x is the eigenvector.

3. Can a linear homogeneous system have more than one repeated eigenvalue?

Yes, a linear homogeneous system can have more than one repeated eigenvalue. This occurs when the matrix has multiple identical eigenvalues, resulting in multiple linearly independent eigenvectors associated with each repeated eigenvalue.

4. What is the significance of repeated eigenvalues in a linear homogeneous system?

Repeated eigenvalues in a linear homogeneous system indicate that the matrix is not diagonalizable. This means that the matrix cannot be transformed into a diagonal matrix using a similarity transformation, which can complicate the analysis of the system.

5. How do repeated eigenvalues affect the stability of a linear homogeneous system?

Repeated eigenvalues can affect the stability of a linear homogeneous system by making it more difficult to determine the behavior of the system over time. The presence of repeated eigenvalues can lead to complex dynamics and potentially unstable solutions, requiring careful analysis and consideration of alternative methods for stability analysis.

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