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Section Solving Linear Systems

[provisional cross-reference: WORK-IN-PROGRESS]
We’ve explored what linear systems are, how to visualize their behavior, and how to write them in compact matrix form. Now it’s time to actually solve them.
We’ll start with a method that mirrors how we solved single constant-coefficient equations: we guess exponential solutions. This guess transforms the differential system into an algebra problem β€” one that will lead naturally to eigenvalues and eigenvectors.

Subsection Guessing Exponential Solutions

Recall how we solved single first-order linear equations like \(y' = ky\) by guessing solutions of the form \(y(t) = Ce^{kt}\text{.}\) We’ll try the same idea for systems.
Consider the system:
\begin{equation} \begin{array}{l} \dfrac{dx}{dt} = 3x + 4y\\ \dfrac{dy}{dt} = -4x + 3y \end{array}\tag{46} \end{equation}
Let’s guess that \(x(t)\) and \(y(t)\) share the same exponential rate \(r\text{:}\)
\begin{equation*} x(t) = Ce^{rt}, \qquad y(t) = De^{rt}. \end{equation*}

πŸ“

Differentiating gives:
\begin{equation*} x' = Cr e^{rt}, \qquad y' = Dr e^{rt}. \end{equation*}
Substituting into the system:
\begin{alignat*}{3} Cr e^{rt} \amp {}={} \amp 3Ce^{rt} \amp {}+{} \amp 4De^{rt}\\ Dr e^{rt} \amp {}={} \amp -4Ce^{rt} \amp {}+{} \amp 3De^{rt} \end{alignat*}
Divide through by \(e^{rt}\) (which is never zero):
\begin{alignat*}{3} Cr \amp {}={} \amp 3C \amp {}+{} \amp 4D\\ Dr \amp {}={} \amp -4C \amp {}+{} \amp 3D \end{alignat*}
This leaves an algebraic system involving the unknowns: \(C\text{,}\) \(D\text{,}\) and \(r\text{.}\) It turns out that these unknowns have a very special relationship. To see this, flip the system around:
\begin{alignat*}{3} 3{\DLBb C} \amp {}+{} \amp 4{\DLGb D} \amp {}={} \amp r{\DLBb C} \\ -4{\DLBb C} \amp {}+{} \amp 3{\DLGb D} \amp {}={} \amp r{\DLGb D} \end{alignat*}
Now, convert this into a \(2 \times 2\) matrix system:
\begin{equation*} \begin{bmatrix} 3 \amp 4\\ -4 \amp 3 \end{bmatrix} \begin{bmatrix} {\DLBb C}\\ {\DLGb D} \end{bmatrix} = r \begin{bmatrix} {\DLBb C}\\ {\DLGb D} \end{bmatrix} \end{equation*}
This is the eigenvalue equation \(A \vec{v} = r \vec{v}\) for the coefficient matrix
\begin{equation*} A = \begin{bmatrix} 3 \amp 4 \\ -4 \amp 3 \end{bmatrix}. \end{equation*}
This tells us something powerful:
  • Linear systems often have solutions made of exponentials.
  • The allowed exponential rates \(r\) are the eigenvalues of \(A\text{.}\)
  • The corresponding coefficients \(C\) and \(D\) form the eigenvectors.

🌌 Example 290. Solving a Simple Linear System.

Solve the system:
\begin{align*} \frac{dx}{dt} \amp = x + y\\ \frac{dy}{dt} \amp = 4x + y \end{align*}
Solution.
Write in matrix form \(\frac{dY}{dt} = AY\) with \(A = \begin{bmatrix}1 \amp 1 \\ 4 \amp 1\end{bmatrix}\text{.}\)
Guess \(Y(t) = \vec{v}e^{rt}\text{.}\) Substituting gives \(A\vec{v} = r\vec{v}\text{.}\)
Find eigenvalues of \(A\) by solving \(\det(A - rI) = 0\text{.}\)
\begin{equation*} \det \begin{bmatrix} 1-r \amp 1 \\ 4 \amp 1-r \end{bmatrix} = (1-r)^2 - 4 = r^2 - 2r - 3. \end{equation*}
Set equal to zero: \(r^2 - 2r - 3 = 0\text{.}\) Factor:
\begin{equation*} (r - 3)(r + 1) = 0 \quad \Rightarrow \quad r_1 = 3, \ r_2 = -1. \end{equation*}
Find eigenvectors for each eigenvalue.
  • For r = 3: Solve \((A - 3I)\vec{v} = 0\text{.}\)
    \begin{equation*} \begin{bmatrix} -2 \amp 1 \\ 4 \amp -2 \end{bmatrix} \begin{bmatrix} v_1 \\ v_2 \end{bmatrix} = \begin{bmatrix} 0 \\ 0 \end{bmatrix}. \end{equation*}
    From the first row: \(-2v_1 + v_2 = 0 \Rightarrow v_2 = 2v_1\text{.}\) Choose \(v_1 = 1\text{,}\) giving eigenvector \(\vec{v}_1 = \begin{bmatrix}1 \\ 2\end{bmatrix}\text{.}\)
  • For r = -1: Solve \((A + I)\vec{v} = 0\text{.}\)
    \begin{equation*} \begin{bmatrix} 2 \amp 1 \\ 4 \amp 2 \end{bmatrix} \begin{bmatrix} v_1 \\ v_2 \end{bmatrix} = \begin{bmatrix} 0 \\ 0 \end{bmatrix}. \end{equation*}
    From the first row: \(2v_1 + v_2 = 0 \Rightarrow v_2 = -2v_1\text{.}\) Choose \(v_1 = 1\text{,}\) giving eigenvector \(\vec{v}_2 = \begin{bmatrix}1 \\ -2\end{bmatrix}\text{.}\)
Write the general solution.
\begin{equation*} Y(t) = C_1 \vec{v}_1 e^{3t} + C_2 \vec{v}_2 e^{-t}. \end{equation*}
Substitute the eigenvectors:
\begin{equation*} \begin{bmatrix} x(t) \\ y(t) \end{bmatrix} = C_1 \begin{bmatrix} 1 \\ 2 \end{bmatrix} e^{3t} + C_2 \begin{bmatrix} 1 \\ -2 \end{bmatrix} e^{-t}. \end{equation*}
Or written componentwise:
\begin{equation*} x(t) = C_1 e^{3t} + C_2 e^{-t}, \qquad y(t) = 2C_1 e^{3t} - 2C_2 e^{-t}. \end{equation*}
The constants \(C_1\) and \(C_2\) will be found from initial conditions.

🌌 Example 291. Complex Eigenvalues and Spirals.

Consider the system:
\begin{align*} \frac{dx}{dt} \amp = 2x - 5y\\ \frac{dy}{dt} \amp = 5x + 2y \end{align*}
Solve the system and describe the behavior of solutions.
Solution.
Write \(A = \begin{bmatrix}2 \amp -5 \\ 5 \amp 2\end{bmatrix}\text{.}\)
Find eigenvalues by solving \(\det(A - rI) = 0\text{.}\)
\begin{equation*} \det \begin{bmatrix} 2-r \amp -5 \\ 5 \amp 2-r \end{bmatrix} = (2-r)^2 + 25 = r^2 - 4r + 29. \end{equation*}
Set equal to zero: \(r^2 - 4r + 29 = 0\text{.}\) Using the quadratic formula:
\begin{equation*} r = \frac{4 \pm \sqrt{(-4)^2 - 4(29)}}{2} = \frac{4 \pm \sqrt{-100}}{2} = 2 \pm 5i. \end{equation*}
The eigenvalues are \(2 \pm 5i\) β€” complex conjugates. The real part (\(2\)) means solutions grow exponentially; the imaginary part (\(5i\)) means they also rotate: the system’s trajectories spiral outward.
Find an eigenvector for \(r = 2 + 5i\) (the other is its conjugate). Solving \((A - rI)\vec{v} = 0\) gives an eigenvector \(\vec{v} = \begin{bmatrix} 1 \\ -i \end{bmatrix}\text{.}\)
Write the solution.
We form the real solution by taking the real and imaginary parts:
\begin{equation*} \begin{aligned} x(t) \amp = e^{2t}\left( C_1 \cos(5t) + C_2 \sin(5t) \right) \\ y(t) \amp = e^{2t}\left( -C_1 \sin(5t) + C_2 \cos(5t) \right) \end{aligned} \end{equation*}
The exponential factor \(e^{2t}\) causes growth; the sine and cosine produce rotation. The system’s phase portrait shows spirals moving outward from the origin.

Subsection πŸ“€ Wrap-Up

πŸ—οΈ \(\textbf{Key Takeaways...}\)
  • For constant-coefficient linear systems, guessing exponential solutions reduces the DE to an eigenvalue problem
  • Eigenvalues, \(r\text{,}\) tell us whether solutions decay, grow, or spiral.

Check Your Understanding.

Checkpoint 292. πŸ€”πŸ’­ Solving Linear Systems Reading Questions.
(a) πŸ“–β“ Eigenvalues and Behavior.
Suppose the eigenvalues of a system are \(r = -2 \pm 3i\text{.}\) What does this tell you about the solutions?
  • They spiral inward toward the origin.
  • They spiral outward away from the origin.
  • The negative real part means decay, not growth.
  • They move straight toward the origin with no oscillation.
  • The imaginary part implies rotation (spiraling).
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