Generalized Eigenvector Solutions for a System of Differential Equations

In summary, the conversation discusses solving a system of differential equations using the form ##x' = Ax## and finding eigenvalues and eigenvectors. The Jordan Canonical Form is mentioned as a way to solve the system, and the conversation ends with the mention of playing around with this method.
  • #1
STEMucator
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Homework Statement



Solve the system:

##x' = 5x - y##
##y' = 4x + y##

Homework Equations



##t## is transpose.

The Attempt at a Solution



I'm a bit rusty with these and I had a small question.

I put the system into the form ##x' = Ax## and proceeded to solve for the eigenvalues. I found that ##\lambda_1 = 3## was the only eigenvalue of multiplicity 2.

I then solved for the eigenspace ##ε_A(\lambda_1) = null(A - \lambda_1I)## and found that the only eigenvector in the span was ##v_1 = [1, 2]^t##.

This yielded my first independent solution ##x_1(t) = e^{3t} [1, 2]^t##.

I need a second independent solution now of the form ##x_2(t) = e^{3t}(tv_1 + u_1)## where ##u_1## is a solution of the system ##(A - \lambda_1I) = v_1##.

Super easy system to solve as it required only two row operations and it left me with:

[2 -1 | 1]
[0 0 | 0]

Hence ##u_1 = span\{ [1/2, 0]^t, [1/2, 1]^t \}## where I've noticed the second vector in the span is simply ##\frac{1}{2}v_1##.

Now my problem. The book lists ##u_1 = [0, -1]^t## as the generalized eigenvector they have used to obtain ##x_2(t)##. I understand how they have obtained this solution, but I'm wondering if my solution is also correct. I would wind up using ##[1/2, 0]^t## as my generalized eigenvector or any multiple of it. Preferably I would use ##[1, 0]^t##.

Then of course ##X(t) = (x_1(t), x_2(t))## and ##X(t)C## is the general solution.
 
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  • #2
Zondrina said:

Homework Statement



Solve the system:

##x' = 5x - y##
##y' = 4x + y##

Homework Equations



##t## is transpose.

The Attempt at a Solution



I'm a bit rusty with these and I had a small question.

I put the system into the form ##x' = Ax## and proceeded to solve for the eigenvalues. I found that ##\lambda_1 = 3## was the only eigenvalue of multiplicity 2.

I then solved for the eigenspace ##ε_A(\lambda_1) = null(A - \lambda_1I)## and found that the only eigenvector in the span was ##v_1 = [1, 2]^t##.

This yielded my first independent solution ##x_1(t) = e^{3t} [1, 2]^t##.

I need a second independent solution now of the form ##x_2(t) = e^{3t}(tv_1 + u_1)## where ##u_1## is a solution of the system ##(A - \lambda_1I) = v_1##.

Super easy system to solve as it required only two row operations and it left me with:

[2 -1 | 1]
[0 0 | 0]

Hence ##u_1 = span\{ [1/2, 0]^t, [1/2, 1]^t \}## where I've noticed the second vector in the span is simply ##\frac{1}{2}v_1##.

Now my problem. The book lists ##u_1 = [0, -1]^t## as the generalized eigenvector they have used to obtain ##x_2(t)##. I understand how they have obtained this solution, but I'm wondering if my solution is also correct. I would wind up using ##[1/2, 0]^t## as my generalized eigenvector or any multiple of it. Preferably I would use ##[1, 0]^t##.

Then of course ##X(t) = (x_1(t), x_2(t))## and ##X(t)C## is the general solution.

If you write the system as ##v' = Av##, where
[tex] v = \pmatrix{x\\y}, \:\text{ and }\: A = \pmatrix{5&-1\\4&1} [/tex]
then the solution is
[tex] v = v_0 e^{At}[/tex]

I don't know if you have yet studied the Jordan Canonical Form, but in this case it is
[tex] J = \pmatrix{3&1\\0&3}[/tex]
so there is an invertible matrix ##P## such that ##A = P J P^{-1}##, hence
[tex] e^{At} \equiv I + \sum_{n=1}^{\infty} \frac{1}{n!} (At)^n = I + \sum_{n=1}^{\infty} \frac{t^n}{n!} A^n = I + \sum_{n=1}^{\infty} \frac{t^n}{n!} P J^n P^{-1}
= P e^{Jt} P^{-1}[/tex]
The matrix ##e^{Jt}## is easy to get:
[tex] e^{tJ} = \pmatrix{e^{3t} & t e^{3t}\\0 & e^{3t}} [/tex]
Therefore, the general homogeneous solution is of the form
[tex] x = a\, e^{3t} + b\, t e^{3t} \\
y = c\, e^{3t} +h \, t e^{3t} [/tex]
for some constants ##a,b,c,h##.

Note: the Jordan form just comes from the generalized eigenvalue problem: if ##u_1## is a generalized eigenvector---so that for eigenvalue ##r## we have ##(A - rI)^2 u_1 = 0##---then setting ##(A - rI)u_1 = u_2## we see that ##u_2## is an eigenvector and that ##Au_1 = r u_1 + u_2##. Together with ##A u_2 = r u_2## we see that the matrix ##A## expressed in the basis ##{u_1, u_2}## is the Jordan form ##J##.
 
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  • #3
Ray Vickson said:
If you write the system as ##v' = Av##, where
[tex] v = \pmatrix{x\\y}, \:\text{ and }\: A = \pmatrix{5&-1\\4&1} [/tex]
then the solution is
[tex] v = v_0 e^{At}[/tex]

I don't know if you have yet studied the Jordan Canonical Form, but in this case it is
[tex] J = \pmatrix{3&1\\0&3}[/tex]
so there is an invertible matrix ##P## such that ##A = P J P^{-1}##, hence
[tex] e^{At} \equiv I + \sum_{n=1}^{\infty} \frac{1}{n!} (At)^n = I + \sum_{n=1}^{\infty} \frac{t^n}{n!} A^n = I + \sum_{n=1}^{\infty} \frac{t^n}{n!} P J^n P^{-1}
= P e^{Jt} P^{-1}[/tex]
The matrix ##e^{Jt}## is easy to get:
[tex] e^{tJ} = \pmatrix{e^{3t} & t e^{3t}\\0 & e^{3t}} [/tex]
Therefore, the general homogeneous solution is of the form
[tex] x = a\, e^{3t} + b\, t e^{3t} \\
y = c\, e^{3t} +h \, t e^{3t} [/tex]
for some constants ##a,b,c,h##.

Note: the Jordan form just comes from the generalized eigenvalue problem: if ##u_1## is a generalized eigenvector---so that for eigenvalue ##r## we have ##(A - rI)^2 u_1 = 0##---then setting ##(A - rI)u_1 = u_2## we see that ##u_2## is an eigenvector and that ##Au_1 = r u_1 + u_2##. Together with ##A u_2 = r u_2## we see that the matrix ##A## expressed in the basis ##{u_1, u_2}## is the Jordan form ##J##.

I haven't seen this, but some wiki research has informed me of how this works. It's quite an interesting formulation actually.

I'm going to go play around with this for awhile now, thank you.
 

What is a generalized eigenvector?

A generalized eigenvector is a vector associated with a given matrix that satisfies the generalized eigenvalue equation. This equation is represented as (A-lambdaI)^kx = 0, where A is the matrix, lambda is the eigenvalue, I is the identity matrix, k is a positive integer, and x is the eigenvector.

How is a generalized eigenvector different from a regular eigenvector?

A regular eigenvector only satisfies the equation Ax = lambda*x, where A is the matrix and lambda is the eigenvalue. In contrast, a generalized eigenvector also satisfies the equation (A-lambdaI)^kx = 0, where k is a positive integer. This allows us to find eigenvectors for matrices that do not have a full set of linearly independent eigenvectors.

When are generalized eigenvectors used?

Generalized eigenvectors are used when a matrix does not have a full set of linearly independent eigenvectors. They can also be used to find the Jordan canonical form of a matrix, which is useful in solving systems of linear differential equations.

What is the relationship between generalized eigenvectors and eigenvalues?

Generalized eigenvectors are associated with eigenvalues, meaning that they are only considered when solving for a specific eigenvalue. The generalized eigenvalue equation is used to find these eigenvectors for a given eigenvalue.

How are generalized eigenvectors calculated?

To calculate generalized eigenvectors, we first need to find the eigenvalues of the matrix. Then, we solve the generalized eigenvalue equation for each eigenvalue to find the associated eigenvectors. These eigenvectors can then be used to find the Jordan canonical form of the matrix.

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