Solving System of ODEs: Matrix Form, Eigenvalues/Vectors

In summary: It's a typo.In summary, the conversation discusses a system of differential equations and finding its matrix form, eigenvalues, and eigenvectors. The general solution is also derived in both matrix and scalar form. There is also a brief discussion about checking the solution and correcting a typo in the equation.
  • #1
shamieh
539
0
Getting stuck on something I think that could be trivial. Maybe someone can see my mistake.

consider the system: $x' = -2x + y$ and $y' = 2x - 3y$

a) Write the system in matrix form
my solution

$\overrightarrow{X} = (^x_y)$

so: $X' = (^{x'}_{y'})$

so $A = $ \begin{bmatrix} -2 & 1 \\ 2 & -3 \end{bmatrix}

b) Find the eigenvalues for the system.
my solution
$\lambda_1 = -1$ and $\lambda_2 = -4$

c) Find an eigenvector for the smallest eigenvalue.
my solution ( Here is where I have either made a algebra mistake or I don't know how to proceed)
$(I-A)X \rightarrow (-I-A)X =$ (\begin{bmatrix} -1 & 0 \\ 0 & - 1 \end{bmatrix} $-$ \begin{bmatrix} -2 & 1 \\ 2 & - 3 \end{bmatrix} ) $X$

Then I am getting: $v_1 - v_2 = 0$ and $-2v_1 + 2v_2 = 0 $ which can be rewritten as one equation $\rightarrow -2v_1+2v_2=0$ so it seems the only numbers I can plug in are: $v_1 = v_2 = -1$ ? But then won't I have like $(^{-1}_{-1})$ and I thought you couldn't do that? Is this the repeated root thing and if so how do I proceed? Or have i made a algebra mistake.

Thanks in advance.
 
Physics news on Phys.org
  • #2
Hi shamieh,

You are correct. The eigenvector for -1 is $\binom {-1}{-1}$. Or alternatively $\binom 11$. If you multiply A with it, you'll get its negative equivalent, as expected.
What do you mean with a repeated root?
 
  • #3
Ignore my comment about the repeated root, I'm thinking too hard :rolleyes: .. So similarly would the eigenvector for the largest value be $(^1_{-2})$ ?

- - - Updated - - -

so I have two more questions with regards to this problem.

e) Write the general solution to the system in matrix form.
I got: $X(t) = C_1(^1_1)e^{-t} + C_2 (^1_{-2})e^{-4t}$but part f) says : Write the general solution to the system in scalar form.. What exactly does that mean? How do I do that?
 
  • #4
shamieh said:
Ignore my comment about the repeated root, I'm thinking too hard :rolleyes: .. So similarly would the eigenvector for the largest value be $(^1_{-2})$ ?

Yup. (Nod)

so I have two more questions with regards to this problem.

e) Write the general solution to the system in matrix form.
I got: $X(t) = C_1(^1_1)e^{-t} + C_2 (^1_{-2})e^{-4t}$

Did you check if it fits into the original DE?
If it fits, it must be right.
but part f) says : Write the general solution to the system in scalar form.. What exactly does that mean? How do I do that?

The solution you have in e) is an equation containing vectors.
So I believe you're supposed to rewrite it into a set of equations that do not contain vectors:
\begin{cases}
x_1(t) = C_1 e^{-t} + C_2 e^{-4t}\\
x_2(t) = C_1 e^{-t} -4 C_2 e^{-4t}
\end{cases}
See? Only scalars.
 
  • #5
I like Serena said:
Yup. (Nod)
Did you check if it fits into the original DE?
If it fits, it must be right.

The solution you have in e) is an equation containing vectors.
So I believe you're supposed to rewrite it into a set of equations that do not contain vectors:
\begin{cases}
x_1(t) = C_1 e^{-t} + C_2 e^{-4t}\\
x_2(t) = C_1 e^{-t} -4 C_2 e^{-4t}
\end{cases}
See? Only scalars.

But do you think I should write it as: $X(t) = 2C_1 e^{-t} - C_2 e^{-4t}$ ? Also why do you have a 4 in front of the $C_2$ in your $x_2(t)$ eqn ?
 
  • #6
shamieh said:
But do you think I should write it as: $X(t) = 2C_1 e^{-t} - C_2 e^{-4t}$ ?

Huh? :confused:
X(t) is a vector while the right hand side is a scalar. That can't be right.
And where do you coefficients come from?

Also why do you have a 4 in front of the $C_2$ in your $x_2(t)$ eqn ?

Sorry. That should be a 2.
 

FAQ: Solving System of ODEs: Matrix Form, Eigenvalues/Vectors

What is the matrix form for solving a system of ODEs?

The matrix form for solving a system of ODEs involves writing the system as a matrix equation, where the coefficient matrix is composed of the derivatives of the variables and the right-hand side vector contains the constant terms. This allows us to solve for the variables by using matrix operations.

Why is it useful to use eigenvalues and eigenvectors when solving a system of ODEs?

Eigenvalues and eigenvectors are useful in solving a system of ODEs because they allow us to reduce the system into a simpler form, making it easier to solve. By finding the eigenvalues and eigenvectors of the coefficient matrix, we can transform the system into a diagonal form, where each variable is decoupled from the others.

How do we find the eigenvalues and eigenvectors of a matrix?

To find the eigenvalues and eigenvectors of a matrix, we first need to find the characteristic polynomial of the matrix. This can be done by subtracting the identity matrix from the coefficient matrix and taking its determinant. The roots of this polynomial are the eigenvalues. Once we have the eigenvalues, we can find the corresponding eigenvectors by solving the equation (A - λI)x = 0, where A is the coefficient matrix and λ is an eigenvalue.

Can we solve a system of nonlinear ODEs using matrix form and eigenvalues/eigenvectors?

Yes, we can solve a system of nonlinear ODEs using matrix form and eigenvalues/eigenvectors. However, the process is more complex and may require numerical methods. The coefficient matrix will not be constant in this case, and the eigenvalues and eigenvectors will vary at each point in the solution.

Are there any limitations to using matrix form and eigenvalues/eigenvectors to solve ODE systems?

One limitation of using matrix form and eigenvalues/eigenvectors to solve ODE systems is that it may not be applicable to all types of systems. Some systems may not have a diagonalizable coefficient matrix, making it difficult to find eigenvalues and eigenvectors. In these cases, alternative methods such as numerical methods may be more suitable.

Back
Top