Eigenvectors/values and diff equations

  • Thread starter EvLer
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In summary, the higher order equation y'' + y = 0 can be written as a first order system by introducing y' as another unknown besides y. This can be represented by the matrix equation d[y y']t/dt = [y' y'']t = [y' -y]t, which can be simplified to du/dt = Au. The 2x2 matrix A for this equation is [0 1; -1 0]. This may result in complex eigenvalues, but this does not affect the validity of the solution. The problem also asks to find the eigenvectors and compute the solution starting from y(0) and y'(0)=0, which can be done using the formulas for
  • #1
EvLer
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Homework Statement


the higher order equation y'' + y = 0 can be written as a first order system by introducing y' as another unknown besides y:

Homework Equations


d[y y']t/dt = [y' y'']t = [y' -y]t

if this is du/dt = Au what is 2x2 matrix A?

The Attempt at a Solution


I think it's
[0 1]
[-1 0]
but then I get complex eigenvalues so I am a bit suspicious... could someone verify or help me out?
thanks.
 
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  • #2
You are exactly correct. If you let x= dy/dt, then d2y/dt2= dx/dt+ y= 0 or dx/dt= -y so your two equations are dy/dt= x and dx/dt= -1. Letting Y(t)= (y(t) x(t))t, yes, your differential equation is
[tex]Y(t)= \left[\begin{array}{cc} 0 & 1 \\ -1 & 0\end{array}\right]Y(t)[/tex]
and, yes, that has complex eigenvalues. Why does that bother you?
I notice that the problem only asks you to write the matrix equation, not to find the eigenvalues or solve the equation so I guess you will learn what complex eigenvalues mean in this situation later.
 
  • #3
I'm trying to finish the very same problem. In addition to what is stated above, the problem says: "Find its eigenvectors, and compute the solution that starts from y(0), y'(0)=0."

I found the eigenvalues to be i and -i, and the eigenvectors to be
[1 -i]^T and [1 i]^T.

I wrote the solution to start as y(t)=(1/2)*e^it*[1 i]^T + (1/2)*e^-it*[1 -i]^T.

From there I don't know how to proceed. Any suggestions?
 
  • #4
eit = cos(t) + isin(t)

Note that (1/2)(eit + e-it) = cos(t)
and that (1/(2i))(eit - e-it) = sin(t)
 

1. What is the purpose of using eigenvectors and eigenvalues in differential equations?

Eigenvectors and eigenvalues are used to find a set of solutions to a system of differential equations. They represent the "directions" in which the system evolves, and the corresponding eigenvalues determine the rate of growth or decay in each direction. This allows for a more efficient and intuitive way to understand and solve complex differential equations.

2. How are eigenvectors and eigenvalues related to each other?

Eigenvectors and eigenvalues are closely related as they are used to solve the same problem. An eigenvector is a vector that does not change its direction when multiplied by a given matrix, and the corresponding eigenvalue is the scalar that scales the eigenvector. In other words, the eigenvector represents the direction and the eigenvalue represents the magnitude of the change in that direction.

3. Can eigenvectors and eigenvalues be complex numbers?

Yes, eigenvectors and eigenvalues can be complex numbers. In fact, complex eigenvalues and eigenvectors are often used to solve systems of differential equations with oscillatory solutions. However, for most practical applications, real eigenvalues and eigenvectors are sufficient.

4. How are eigenvectors and eigenvalues used in solving systems of differential equations?

Eigenvectors and eigenvalues are used to diagonalize the coefficient matrix of a system of differential equations. This means that the matrix is transformed into a diagonal matrix, where the entries on the main diagonal are the eigenvalues. This simplifies the system of differential equations and allows for an easier solution.

5. Can multiple eigenvectors correspond to the same eigenvalue?

Yes, it is possible for multiple eigenvectors to correspond to the same eigenvalue. This is known as an eigenvalue with multiplicity, and it often occurs in larger systems of differential equations. In these cases, the number of linearly independent eigenvectors will be equal to the multiplicity of the eigenvalue.

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