Fundamental matrix for complex linear DE system

In summary, the fundamental matrix for a complex linear differential equation system is a matrix solution that encapsulates the behavior of a system of linear ordinary differential equations with complex coefficients. It is constructed from a set of linearly independent solutions to the system and provides a means to express the general solution. The fundamental matrix is essential for analyzing the stability and dynamics of the system, as it allows for the computation of the system's response to initial conditions and external inputs.
  • #1
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Homework Statement
Please see below
Relevant Equations
Please see below
For this problem,
1715821129093.png

I am trying to find the fundamental matrix, however, the eigenvalues are both imaginary and so are the eigenvectors. That is, ##\lambda_1 = 4i, \lambda_2 = -4i##

##v_1 = (1 + 2i, 2)^T##
##v_2 = (1 - 2i, 2)^T##

So I think I just have an imaginary matrix? This is because the general solution is ##\vec x = c_1(1 + 2i, 2)^T e^{4it} + c_2(1 - 2i, 2)^T e^{-4it}##, then does someone please know whether I can just write the system in the form ##\vec x = \vec Φ \vec c## where ##c = (c_1, c_2)^T## and Φ is complex?

Thanks!
 
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  • #2
Why is it a problem that eigenvalues are not real? A fundamental matrix of the system is a matrix-valued function ##\Phi## such that the columns of ##\Phi(t)## are linearly independent solutions of the given system for all ##t##. Simply write how ##\Phi(t)## is parametrised with respect to ##t## and you are done.
 
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  • #3
The algebra and analysis with complex values work at least as well as with real values. If you are familiar with the complex exponential function, you should be able to do the analysis.
 
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  • #4
Here are a few things to note

- By finding an eigenvalue ##\lambda_1## and an associated eigenvector ##\vec{v}_1##, you have found a complex solution of form ##e^{\lambda_1 t}\vec{v_1}##.

- The other eigenvalue and eigenvector is another complex solution.

- If we are looking for two real solutions then it turns out that given one single complex solution, the real and imaginary parts are each a real solution. In addition, these two real solutions are linearly independent.

- Therefore, all you have to do is take one of the complex solutions, expand the terms and do algebra until you have a real and an imaginary part clearly separated. A little more detail on what this entails:

-- you will have something like ##e^{zt}\vec{v}## where ##z\in\mathbb{C}##.

-- write ##e^{zt}## as a real exponential times an imaginary exponential (which you expand into sine and cosine using Euler's formula)

-- write the eigenvector ##\vec{v}## as the sum of a real vector and an imaginary vector

-- Now you can multiply everything so that you get a real part and an imaginary part for the expression as a whole.

- At that point, you have two real solutions which when placed as columns of a matrix makes this matrix a fundamental matrix.
 
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  • #5
You know from the Cayley-Hamilton theorem that [itex]A = \begin{pmatrix}2 & - 5 \\ 4 & - 2 \end{pmatrix}[/itex] satisfies its own characteristic polynomial, so that [itex]A^2 = -4^2I[/itex]. It follows that you can compute [itex]\Phi(t) = \exp(At)[/itex] directly: [tex]\begin{split}
\exp(At) &= \sum_{n=0}^\infty \frac{A^{2n}t^{2n}}{(2n)!} + \sum_{n=0}^\infty \frac{A^{2n+1}t^{2n+1}}{(2n+1)!} \\
&= \sum_{n=0}^\infty \frac{(-1)^n4^{2n}t^{2n}}{(2n)!} + A \sum_{n=0}^\infty \frac{(-1)^n4^{2n}t^{2n+1}}{(2n+1)!} \\
&= I\cos 4t + \frac{\sin 4t}{4} A. \end{split}[/tex]
Indeed we can do this for any 2x2 matrix [itex]A[/itex], because we can write the characteristic polynomial in the form [tex](A - bI)^2 = cI.[/tex] Since the relevant matrices commute we have [tex]e^{-bt}\exp(At) = \exp(-btI)\exp(At) = \exp(At)\exp(-btI) = \exp((A - bI)t)[/tex] and in the same manner as the above, [tex]\begin{split}
\exp((A - bI)t) &=
\sum_{n=0}^\infty \frac{c^nt^{2n}}{(2n)!}I + \sum_{n=0}^\infty \frac{c^{n}t^{2n+1}}{(2n+1)!}A \\
&= \begin{cases} \cosh(\sqrt{c}t)I + \frac{\sinh(\sqrt{c}t)}{\sqrt{c}}A & c \neq 0 \\
I + (A- bI)t & c = 0.
\end{cases}
\end{split}[/tex] Hence [tex]
\exp(At) = \begin{cases}
e^{bt}\cosh(\sqrt{c}t)I + \frac{e^{bt}\sinh(\sqrt{c}t)}{\sqrt{c}}A & c \neq 0 \\
(1 - bt)e^{bt}I + Ate^{bt} & c = 0.\end{cases}[/tex] Note also the identities [tex]
\cosh(iz) = \cos z \qquad \sinh(iz) = i\sin z.[/tex]
 
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FAQ: Fundamental matrix for complex linear DE system

What is a fundamental matrix for a complex linear differential equation system?

A fundamental matrix for a complex linear differential equation system is a matrix solution to a system of first-order linear differential equations. It is constructed from the solutions of the system and serves as a tool to express the general solution of the system. Each column of the fundamental matrix corresponds to a particular solution of the differential equation system, and the matrix itself captures the behavior of the system in a compact form.

How do you compute the fundamental matrix for a given system of linear differential equations?

To compute the fundamental matrix for a system of linear differential equations, you first need to write the system in matrix form as \( \mathbf{y}' = \mathbf{A} \mathbf{y} \), where \( \mathbf{A} \) is a matrix of coefficients and \( \mathbf{y} \) is the vector of dependent variables. You can then find the fundamental matrix by solving the matrix equation using methods such as the matrix exponential, \( \Phi(t) = e^{\mathbf{A}t} \), where \( \Phi(t) \) is the fundamental matrix and \( t \) is the independent variable. Alternatively, you can find linearly independent solutions and arrange them in a matrix.

What is the significance of the fundamental matrix in the context of linear differential equations?

The fundamental matrix is significant because it provides a systematic way to construct the general solution of a linear differential equation system. By using the fundamental matrix, one can express any solution to the system as a linear combination of the columns of the fundamental matrix. This is particularly useful in analyzing the stability and behavior of the system, as well as in solving initial value problems.

Can the fundamental matrix be used for non-homogeneous systems?

Yes, the fundamental matrix can be adapted for non-homogeneous systems. For a non-homogeneous system of the form \( \mathbf{y}' = \mathbf{A} \mathbf{y} + \mathbf{b}(t) \), where \( \mathbf{b}(t) \) is a non-homogeneous term, you can first find the fundamental matrix for the associated homogeneous system \( \mathbf{y}' = \mathbf{A} \mathbf{y} \). Then, you can use the method of variation of parameters or other techniques to find a particular solution, which can be combined with the general solution derived from the fundamental matrix to obtain the complete solution.

How does the fundamental matrix relate to the eigenvalues and eigenvectors of the coefficient matrix?

The fundamental matrix is closely related to the eigenvalues and eigenvectors of the coefficient matrix \( \mathbf{A} \). When the eigenvalues of \( \mathbf{A} \) are

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