Computing eigenvalues and eigenvectors

In summary, eigenvalues and eigenvectors are concepts used in linear algebra to analyze and understand linear transformations. Eigenvalues represent the scaling factor of eigenvectors, which are non-zero vectors that do not change direction when the transformation is applied. To compute eigenvalues and eigenvectors, the characteristic polynomial of the transformation matrix must be found, and they have applications in various fields such as physics, engineering, and data analysis. They are closely related to matrix diagonalization, where the diagonal elements are the eigenvalues and the corresponding eigenvectors form the transformation matrix. Eigenvalues and eigenvectors can also be complex numbers, often encountered in applications such as quantum mechanics and electrical engineering.
  • #1
jinksys
123
0
Find the characteristic polynomial, eigenvectors, and eigenvalues of the matrix.

Code:
[
2 -2 3
0 3 -2
0 -1 2
]

My characteristic poly is x^3 - 7x^2 + 14x - 8

The roots are 1, 2, and 4.

When solving the system, (2I - A)x = 0 I get:
Code:
[
0 1 0 0 
0 0 1 0 
0 0 0 0 
]

Can someone tell me how this relates to a solution?

It looks like x2 and x3 would be zero, but what about x1?
 
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  • #2
This means that (t 0 0) is an eigenvector for any t. Since constant multiples of eigenvectors are also eigenvectors, typically we would say that (1 0 0) is the eigenvector (associated with the eigenvalue 2).
 
  • #3
Thanks, that makes sense.
 

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are concepts in linear algebra that are used to analyze and understand the properties of a linear transformation. Eigenvalues are scalar values that represent the scaling factor of the eigenvector when the linear transformation is applied, while eigenvectors are non-zero vectors that do not change direction when the linear transformation is applied.

2. How do you compute eigenvalues and eigenvectors?

To compute eigenvalues and eigenvectors, you first need to find the characteristic polynomial of the linear transformation. This is done by subtracting the scalar variable lambda from the diagonal elements of the transformation matrix, and then finding the determinant of the resulting matrix. The roots of the characteristic polynomial are the eigenvalues, and the corresponding eigenvectors can be found by solving the linear system of equations formed by substituting the eigenvalues into the original transformation matrix.

3. What are the applications of computing eigenvalues and eigenvectors?

Eigenvalues and eigenvectors have many applications in various fields such as physics, engineering, and data analysis. They are used to solve differential equations, study the stability of systems, and reduce the dimensionality of large data sets. They are also important in understanding the behavior of systems and identifying patterns in data.

4. How do eigenvalues and eigenvectors relate to matrix diagonalization?

Eigenvalues and eigenvectors are closely related to matrix diagonalization. Diagonalization is the process of finding a diagonal matrix that is similar to the original matrix, where the diagonal elements are the eigenvalues and the corresponding eigenvectors form the columns of the transformation matrix. This allows for easier computation and analysis of the linear transformation.

5. Can eigenvalues and eigenvectors be complex numbers?

Yes, eigenvalues and eigenvectors can be complex numbers. In fact, complex eigenvalues and eigenvectors are often encountered in applications such as quantum mechanics and electrical engineering. Complex eigenvalues and eigenvectors represent the rotation and scaling of vectors in the complex plane, and they can also be used to describe oscillatory behavior in systems.

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