Non-linearly independent eigenvectors

In summary, the problem involves finding a non-singular matrix P such that P-1BP = D, where D is a diagonal matrix. By using the determinant equation, it is determined that the matrix has eigenvalues of 1 and -1. The eigenvectors for these eigenvalues are (1/3, 1) and (1, 0) respectively. After revising the solution space, it is found that these are the only eigenvectors, making them non-linearly independent.
  • #1
derryck1234
56
0

Homework Statement



Let B = 1 0
6 -1
Be a square matrix. Find a non-singular matrix P such that P-1BP = D, where D is a diagonal matrix and show that P-1BP = D.

Homework Equations



det(lambdaI - A) = 0

The Attempt at a Solution



Ok, this might look like a simple problem...but I get non-linearly independent eigenvectors, even although I have 2 distinct eigenvalues?

Noting that it is a triangular matrix, the diagonal entries thus correspond to its eigenvalues. So, the matrix has lambda = 1 and lambda = -1 as its eigenvalues.

For lambda = 1, I obtain the basis vector (1/3, 1).

For lambda = -1, I obtain the basis vectors (1, 0) and (0, 1)...

Thus, I have non-linearly independent eigenvectors, since (1/3, 1) can be written as 1/3(1, 0) + (0, 1)...

?
 
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  • #2


i got only (0,1) and (1/3,1) as the eigenvectors. (1,0) isn't an eigenvector.
 
  • #3


Ok. I see it now...flip, just needed some revising on finding solution spaces...I get the same answer now too...thanks...
 

FAQ: Non-linearly independent eigenvectors

What are non-linearly independent eigenvectors?

Non-linearly independent eigenvectors are a set of vectors that cannot be expressed as a linear combination of each other. In other words, they cannot be scaled or multiplied by constants to obtain one another.

Why are non-linearly independent eigenvectors important?

Non-linearly independent eigenvectors play a crucial role in solving systems of differential equations and in understanding the behavior of complex systems. They also provide a basis for the eigenspace of a matrix, which is used in various applications.

How do you determine if a set of eigenvectors is non-linearly independent?

To determine if a set of eigenvectors is non-linearly independent, you can use the rank-nullity theorem. If the rank of the matrix formed by the eigenvectors is equal to the number of eigenvectors, then they are non-linearly independent.

Can a matrix have a set of linearly independent eigenvectors that are not non-linearly independent?

Yes, it is possible for a matrix to have a set of linearly independent eigenvectors that are not non-linearly independent. This occurs when the matrix has repeated eigenvalues, which means that there are multiple eigenvectors corresponding to the same eigenvalue.

How are non-linearly independent eigenvectors used in real-world applications?

Non-linearly independent eigenvectors are used in various fields of science and engineering, such as in quantum mechanics, control theory, and computer graphics. They are also used in data analysis and machine learning algorithms to identify patterns and make predictions.

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