Eigenvalues for matrices proof

Thread starter gutnedawg
Start date Apr 8, 2010
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Eigenvalues Matrices Proof

In summary, eigenvalues and eigenvectors are concepts used in linear algebra to describe the behavior of linear transformations. Eigenvalues represent how a vector is scaled by the transformation, while eigenvectors are the corresponding scaled vectors. The process for computing eigenvalues involves finding values that satisfy a specific equation, and they have various applications in math, physics, and engineering. The determinant of a matrix can be used to find its eigenvalues, and a matrix can have repeated eigenvalues if the characteristic polynomial has repeated roots.

Apr 8, 2010

gutnedawg

Let C be a 2 × 2 matrix such that x is an eigenvalue of C with multiplicity two
and dimNul(C − xI) = 1.
Prove that C = P |x 1|P^−1
|0 x|
for some invertible 2 × 2
matrix P.

I'm not sure where to start

EDIT
|x 1|
|0 x| is the matrix I don't know why it's not posting the way I want it

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Apr 9, 2010

Eynstone

Consider P which makes C upper diagonal & use the nullity condition.

What is the definition of eigenvalues and eigenvectors for matrices?

Eigenvalues and eigenvectors are concepts in linear algebra that are used to describe the behavior of linear transformations. Eigenvalues are scalar values that represent how a particular vector is scaled by the transformation, while eigenvectors are the corresponding vectors that are only scaled by the transformation.

How do you compute eigenvalues for a given matrix?

The process for computing eigenvalues for a given matrix involves finding the values of lambda that satisfy the equation (A - lambda*I)x = 0, where A is the matrix, I is the identity matrix, and x is the eigenvector. This equation can be solved using various methods such as the characteristic polynomial or the power method.

What are the applications of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors have many applications in mathematics, physics, and engineering. They are used to solve systems of linear differential equations, to analyze the stability of dynamic systems, and to perform dimensionality reduction in data analysis.

What is the relationship between eigenvalues and determinants?

The eigenvalues of a matrix are the roots of its characteristic polynomial, which is equal to the determinant of (A - lambda*I). This means that the determinant of a matrix can be used to find its eigenvalues, and vice versa. Additionally, the determinant of a matrix is equal to the product of its eigenvalues.

Can a matrix have repeated eigenvalues?

Yes, a matrix can have repeated eigenvalues. This occurs when the characteristic polynomial has repeated roots, meaning that there are multiple eigenvectors associated with the same eigenvalue. In this case, the matrix is called defective and cannot be diagonalized.