Finding Eigenvectors with a Parameter in Homework Solution?

In summary, the conversation is about finding the eigenvalues and eigenvectors of a 3x3 matrix with all entries equal to 1. The eigenvalues are found to be 0 and 3, and the conversation discusses the difference between finding eigenvectors using a parameterized form versus specific coordinates. The suggestion is made to consider a parametrized form that includes all multiples of an eigenvector.
  • #1
Maybe_Memorie
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Homework Statement




Okay, we've got a 3x3 matrix. All the entries are equal to 1. Call the matrix A.
Basically It looks like this
1 1 1
1 1 1
1 1 1

find all the eigenvalues and eigenvectors.

The Attempt at a Solution



Right, I got the eigenvalues being 0 and 3, the same as my lecturer got. That's fine, I just let the determinant of ([tex]\lambda[/tex]I-A) equal 0.
When getting the eigenvectors, every book I read gives the vector in terms of a parameter, such as t. However, my lecturer got vectors with specific coordinates.

What am I supposed to do?
 
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  • #2
Maybe_Memorie said:

Homework Statement




Okay, we've got a 3x3 matrix. All the entries are equal to 1. Call the matrix A.
Basically It looks like this
1 1 1
1 1 1
1 1 1

find all the eigenvalues and eigenvectors.

The Attempt at a Solution



Right, I got the eigenvalues being 0 and 3, the same as my lecturer got. That's fine, I just let the determinant of ([tex]\lambda[/tex]I-A) equal 0.
When getting the eigenvectors, every book I read gives the vector in terms of a parameter, such as t. However, my lecturer got vectors with specific coordinates.

What am I supposed to do?

Let v be an eigenvector of your matrix and let l be its eigenvalue. If A is your matrix then, as you know Av = lv. Now, what if you were to take a multiple of v, is that an eigenvector? That is, if k is a scalar, is kv an eigenvector? If so, can you think of a parametrized form of v that would include all multiples of v?
 

FAQ: Finding Eigenvectors with a Parameter in Homework Solution?

1. What are eigenvectors of a matrix?

Eigenvectors of a matrix are special vectors that when multiplied by the matrix result in a scalar multiple of the original vector. In other words, the direction of the vector remains the same, but the magnitude is multiplied by a constant, known as the eigenvalue.

2. How are eigenvectors and eigenvalues related?

Eigenvectors and eigenvalues are closely related because every eigenvector has a corresponding eigenvalue. The eigenvalue represents the scalar by which the eigenvector is multiplied when multiplied by the matrix. Therefore, the eigenvalue determines the magnitude of the eigenvector.

3. What is the significance of eigenvectors in linear algebra?

Eigenvectors are significant in linear algebra because they provide important insights into the behavior of matrices. They are useful for understanding the transformations that matrices represent and can be used to simplify complex calculations.

4. How are eigenvectors used in data analysis?

Eigenvectors are commonly used in data analysis, particularly in the field of principal component analysis (PCA). In PCA, the eigenvectors of a covariance matrix represent the directions of maximum variation in a dataset, allowing for dimensionality reduction and visualization of high-dimensional data.

5. Can a matrix have more than one eigenvector?

Yes, a matrix can have multiple eigenvectors, each with its corresponding eigenvalue. In fact, most matrices have multiple eigenvectors. However, the maximum number of linearly independent eigenvectors that a matrix can have is equal to its dimension.

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