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10.5: Repeated Eigenvalues with One Eigenvector

  • Page ID
    96188
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    Example: Find the general solution of \(\dot{x}_{1}=x_{1}-x_{2}, \dot{x}_{2}=x_{1}+3 x_{2}\).

    View tutorial on YouTube

    The equations in matrix form are

    \[\frac{d}{d t}\left(\begin{array}{l} x_{1} \\ x_{2} \end{array}\right)=\left(\begin{array}{rr} 1 & -1 \\ 1 & 3 \end{array}\right)\left(\begin{array}{l} x_{1} \\ x_{2} \end{array}\right) \nonumber \]

    The ansatz \(\mathrm{x}=\mathrm{v} e^{\lambda t}\) leads to the characteristic equation

    \[\begin{aligned} 0 &=\operatorname{det}(\mathbf{A}-\lambda \mathbf{I}) \\ &=\lambda^{2}-4 \lambda+4 \\ &=(\lambda-2)^{2} . \end{aligned} \nonumber \]

    Therefore, \(\lambda=2\) is a repeated eigenvalue. The associated eigenvector is found from \(-v_{1}-v_{2}=0\), or \(v_{2}=-v_{1} ;\) and normalizing with \(v_{1}=1\), we have

    \[\lambda=2, \quad \mathrm{v}=\left(\begin{array}{r} 1 \\ -1 \end{array}\right) \nonumber \]

    We have thus found a single solution to the ode, given by

    \[\mathrm{x}_{1}(t)=c_{1}\left(\begin{array}{r} 1 \\ -1 \end{array}\right) e^{2 t} \nonumber \]

    and we need to find the missing second solution to be able to satisfy the initial conditions. An ansatz of \(t\) times the first solution is tempting, but will fail. Here, we will cheat and find the missing second solution by solving the equivalent secondorder, homogeneous, constant-coefficient differential equation.

    Screen Shot 2022-05-29 at 10.33.46 PM.png
    Figure 10.3: Phase portrait for example with only one eigenvector.

    If \(A\) has only a single linearly independent eigenvector \(v\), then Equation \ref{10.13} can be solved for \(w\) (otherwise, it cannot). Using \(A, \lambda\) and \(v\) of our present example, Equation \ref{10.13} is the system of equations given by

    \[\left(\begin{array}{rr} -1 & -1 \\ 1 & 1 \end{array}\right)\left(\begin{array}{l} w_{1} \\ w_{2} \end{array}\right)=\left(\begin{array}{r} 1 \\ -1 \end{array}\right) \nonumber \]

    The first and second equation are the same, so that \(w_{2}=-\left(w_{1}+1\right)\). Therefore,

    \[\begin{aligned} w &=\left(\begin{array}{c} w_{1} \\ -\left(w_{1}+1\right) \end{array}\right) \\ &=w_{1}\left(\begin{array}{r} 1 \\ -1 \end{array}\right)+\left(\begin{array}{r} 0 \\ -1 \end{array}\right) \end{aligned} \nonumber \]

    Notice that the first term repeats the first found solution, i.e., a constant times the eigenvector, and the second term is new. We therefore take \(w_{1}=0\) and obtain

    \[\mathrm{w}=\left(\begin{array}{r} 0 \\ -1 \end{array}\right) \nonumber \]

    as before.

    The phase portrait for this ode is shown in Fig. 10.3. The dark line is the single eigenvector \(\mathbf{v}\) of the matrix \(\mathbf{A}\). When there is only a single eigenvector, the origin is called an improper node.


    This page titled 10.5: Repeated Eigenvalues with One Eigenvector is shared under a CC BY 3.0 license and was authored, remixed, and/or curated by Jeffrey R. Chasnov via source content that was edited to the style and standards of the LibreTexts platform.