Setting up linear systems in a matrix

In summary, the student is getting stuck on the very beginning of these homework problems and is looking for help. They have found their eigenvalues and eigenvectors using the characteristic equation, but need help getting started with setting up a general solution.
  • #1
Zem
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I am getting stuck on the very beginning of these homework problems.

Solve the linear system to determine whether the critical point (0,0) is stable, asymtotically stable, or unstable.

[tex]dx/dt = -2x, dy/dt = -2y[/tex]

The book uses separation of variables, but the professor has instructed us to use matrices to do these homework problems.
[tex] x= \left(\begin{array}{c} -2 \ 0\\ 0 \ -2 \end{array}\right) [/tex]
[tex]\lambda_1 = 0, \lambda_2 = 4[/tex]

Is this the right matrix? When I use lambda = 0 in the eigenvalue method (A - (lambda)I)=0 on that matrix, the vector entries a and b both = 0 because it's just a diagonal matrix. I need to find a general solution such that
x = c1v1e^((lambda)t) + c2v2e^((lambda)t). Then determine the stability of x. But first I need to know how to set it up.
 
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  • #2
You have your function x equal to a 2x2 matrix? That matrix does equal something important, but its certainly not x. And the numbers you found are not the eigenvalues of that matrix. Looking at the matrix, it should be very obvious what the eigenvalue(s) is/are. To help you get started:

[tex]\left(\begin{array}{c}x'\\y'\end{array}\right)=\left(\begin{array}{cc}-2&0\\0&-2\end{array}\right)\left(\begin{array}{c}x\\y\end{array}\right)[/tex]
 
  • #3
I got the eigenvalues with the characteristic equation.
[tex](-2 - \lambda)(-2 - \lambda) -4 = 0[/tex]
[tex]\lambda^2 + 4\lambda = 0[/tex]
[tex]\lambda_1 = 0, \lambda_2 = -4[/tex]

Case 1:
[tex](A - \lambda I = 0)[/tex]
[tex]-2x + 0y = 0[/tex]
[tex]0x - 2y = 0[/tex]
[tex]x = 0, y =0[/tex]
v_1 = [0,0]

Case 2:
[tex]2x + 0y = 0[/tex]
[tex]0x + 2y = 0[/tex]
v_2 = [0,0]

Both eigenvectors equal 0? How can I set up a general solution without eigenvectors = 0? The general solution should be of the form [tex]x(t) = c_1v_1e^(lambda_1 t) + c_2v_2e^(lambda_2 t)[/tex]

[tex]\lambda t[/tex] should be in the exponent of e in the equation above, but I don't know how to make the code do that.
 
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  • #4
That's not the characteristic equation.
 
  • #5
Oops.
[tex]\lambda ^2 + 4\lambda + 4 = 0[/tex]
[tex]\lambda_1 = \lambda_2 = -2[/tex]

Find v_1: [tex](A - \lambda I = 0)[/tex]
[tex] \left(\begin{array}{c} 0 \ 0\\ 0 \ 0 \end{array}\right) [/tex]

How can I get an eigenvector when (A - lambda) = 0?

Since the eigenvalues are equal and less than zero, I know it is a stable proper node. So the graphing part is done. But I need a general solution.
 
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  • #6
substitute back in your eigenvalues (this doesn't give the null matrix) and satisfy:

[tex]A{\bf v}=\lambda {\bf v}[/tex]

or

[tex](A-\lambda I){\bf v}=0[/tex]

where the eigenvector [tex]{\bf v}=(v_1; v_2)[/tex]
 
  • #7
It might be worth your time to notice that if the matrix is
[tex] \left(\begin{array}{c} a \ 0\\ b \ c \end{array}\right) [/tex]
then the eigenvalue equation is
[tex]\left|\begin{array}{c}a-\lambda \ 0\\ b\ c-\lambda\end{array}\right|= (a- \lambda)(c-\lambda)= 0[/tex]
In other words for any triangular (which include diagonal) matrix, the numbers on the main diagonal are the eigenvalues.
 
  • #8
[tex](A-\lambda I){\bf v}=0[/tex]
[tex] \left(\begin{array}{c} -2 \ 0\\ 0 \ -2 \end{array}\right) [/tex]
[tex] \left(\begin{array}{c} -2-(-2) \ 0\\ 0 \ -2-(-2) \end{array}\right) [/tex]
[tex] \left(\begin{array}{c} 0 \ 0\\ 0 \ 0 \end{array}\right) [/tex]

or

[tex](-2 - (-2)){\bf v}=0[/tex]
[tex]0v = 0[/tex]

What am I missing?
 
  • #9
After asking the professor about this, it turns out the matrix is
[tex] \left(\begin{array}{c} 0 \ 0\\ 0 \ 0 \end{array}\right) [/tex]
after
[tex](A-\lambda I){\bf v}=0[/tex]

This means any vector will work.
[tex]v_1 = \left(\begin{array}{c} 1 \\ 0 \end{array}\right) [/tex]
[tex]v_2 = \left(\begin{array}{c} 0 \\ 1 \end{array}\right) [/tex]

General Solution:
x(t) = c_1(1)e^-2t + c_2(0)e^-2t = c_1e^-2t
y(t) = c_1(0)e^-2t + c_2(1)e^-2t = c_2e^-2t
 
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  • #10
That's correct. Note you should be able to look at your matrix, and recognize that it's -2I, where I is the identity matrix. If you know what eigenvectors and eigenvalues are (and not just how to compute them) then you should be able to see immediately that the only eigenvalue is -2, and that every vector is an eigenvector.
 
  • #11
The linear algebra explanation of this is the easiest way for me to understand it. Since [tex](A - \lambda I = 0)[/tex] is -2a - 0b = 0, a is a free variable. So a could be anything, which leads to an eigenvector that could be anything. Thanks everyone!
 

FAQ: Setting up linear systems in a matrix

What is a linear system in a matrix?

A linear system in a matrix is a set of equations that can be represented using a matrix. Each equation in the system corresponds to a row in the matrix, and each variable in the equations corresponds to a column in the matrix.

How do I set up a linear system in a matrix?

To set up a linear system in a matrix, first write down all the equations in the system. Then, arrange the coefficients of each variable in each equation into a matrix. The constants in the equations will form the right-hand side of the matrix.

What are the benefits of setting up a linear system in a matrix?

Setting up a linear system in a matrix allows for a more organized and efficient way to solve systems of equations. It also allows for the use of matrix operations and techniques, such as Gaussian elimination, to solve the system.

Can a linear system in a matrix have more than one solution?

Yes, a linear system in a matrix can have one, infinite, or no solutions. This depends on the coefficients and constants in the system and can be determined through various methods, such as Gaussian elimination or matrix inversion.

How does one solve a linear system in a matrix?

There are various methods for solving a linear system in a matrix, such as Gaussian elimination, matrix inversion, and Cramer's rule. The method used will depend on the size and complexity of the system.

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