Compute the Eigenvalues and Eigenvectors

In summary, to find the eigenvalues and eigenvectors of the given matrix A, we use the equation |A-lambda*I|=0 and solve for the eigenvalues. Then, we plug in each eigenvalue into the equation and solve for the corresponding eigenvector. It is possible for the eigenvector to have a row of zeroes, as long as the other rows are not all zeroes and the vector is still linearly independent.
  • #1
swtjuice
3
0

Homework Statement



Compute the Eigenvalues and Eigenvectors of A
A= [0 0 1;0 2 0;3 0 0]


Homework Equations



|A-lamda*I|=0
where I know the lamdas and plug them into the above equation and expand the system of equations.

The Attempt at a Solution



I have solved for the eigenvalues and got +sqrt(3), -sqrt(3) and 2.
I have solved for the eigenvectors associated with +sqrt(3), -sqrt(3), and checked them in matlab.
For lamda (eigenvalue) of sqrt(3) the eigenvector is
[ 1;0;sqrt(3)]

For lamda (eigenvalue) of -sqrt(3) the eigenvector is
[ -1;0;sqrt(3)]

But for when the eigenvalue is equal to 2 I come up to problems. where the 2nd row of my matrix is all zeroes. This confuses me because I have checked the vector in MATLAB and know it should be [0;1;0]. Which is impossible based on the 2nd row being all zeroes.
 
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  • #2
Can someone explain why this is the case?



It is possible for the eigenvector to have a row of zeroes, as long as the other rows are not all zeroes and the vector is still linearly independent. In this case, the eigenvector for the eigenvalue of 2 is [0;1;0], as you have correctly found. This simply means that the eigenvector is a scalar multiple of the identity matrix, which is still a valid eigenvector.
 

1. What is the importance of computing eigenvalues and eigenvectors?

Computing eigenvalues and eigenvectors allows us to understand the behavior and properties of a linear transformation or a matrix. This is useful in many areas of science and engineering, such as in physics, chemistry, and computer science.

2. How do you compute eigenvalues and eigenvectors?

To compute eigenvalues and eigenvectors, we first need to find the characteristic polynomial of the matrix. Then, we solve for the roots of the polynomial, which are the eigenvalues. Finally, we use these eigenvalues to find the corresponding eigenvectors.

3. What is the difference between eigenvalues and eigenvectors?

Eigenvalues are the scalars that represent the amount of stretch or compression of the eigenvectors of a matrix. Eigenvectors are the non-zero vectors that are scaled by the eigenvalues when multiplied by a matrix.

4. Can a matrix have more than one set of eigenvalues and eigenvectors?

Yes, a matrix can have multiple sets of eigenvalues and eigenvectors. This means that there are multiple ways to represent the behavior of the matrix, depending on the chosen set of eigenvalues and eigenvectors.

5. What is the significance of the eigenvectors corresponding to the largest eigenvalues?

The eigenvectors corresponding to the largest eigenvalues are important because they represent the most significant directions of change in the matrix. They can help us understand the most dominant behavior or pattern in the data represented by the matrix.

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