Finding Eignevalues & Eigenvectors of A⁻¹ Without Direct Computation

In summary, the conversation discusses finding the eigenvalues and eigenvectors for a given matrix without directly computing them. The concept of eigenvalues and eigenvectors is used to determine if the eigenvalues of A-1 are the same, opposite, or inverse of those of matrix A. The conversation also mentions some possible values for the eigenvalues and eigenvectors and the relationship between A and A-1.
  • #1
brad sue
281
0
Hi, I need help for this problem:
Find the eignevalues and eingenvectors for the matrix below. DO NOT compute them directly by computing the matrix:
A-1

We need to find some kind of demonstration to see if the eignevalues of A-1 are the same, opposite or inverse (or whatever) as those of matrix A
Suppose that the eignvalues are 1,2,3 and the eignvectors are [1,1,0], [0,1,0],[ 3,-1,2] ( in columns
Thank you
B
 
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  • #2
If [tex]Ax= \lambda x[/tex] Then [tex]A^{-1}Ax= A^{-1}\lambda x= \lambda A^{-1}x[/tex].

What does that tell you?
 
  • #3
HallsofIvy said:
If [tex]Ax= \lambda x[/tex] Then [tex]A^{-1}Ax= A^{-1}\lambda x= \lambda A^{-1}x[/tex].
What does that tell you?
I have been thinking but I really do not know.
Can we say that I ( indentity matrix)= lambda*A-1??
I does make me go far doesn't it?
 

FAQ: Finding Eignevalues & Eigenvectors of A⁻¹ Without Direct Computation

What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are mathematical concepts used in linear algebra to describe the behavior of linear transformations. Eigenvalues are scalar values that represent how much a given vector is stretched or contracted by a transformation, while eigenvectors are the corresponding vectors that remain in the same direction after the transformation.

Why is it important to find eigenvalues and eigenvectors?

Finding eigenvalues and eigenvectors can help us understand the properties of a linear transformation, such as its stretching or shearing effects. They are also important in solving systems of differential equations and in applications such as signal processing and quantum mechanics.

How can we find eigenvalues and eigenvectors of A⁻¹ without direct computation?

One method is to use the inverse power iteration algorithm, which involves repeatedly multiplying a given vector by the inverse of the matrix A⁻¹ and then normalizing the resulting vector. This process converges to the eigenvector with the largest eigenvalue of A⁻¹. Another method is to use the Cayley-Hamilton theorem, which states that the inverse of a matrix can be expressed in terms of its eigenvalues and eigenvectors.

What are some advantages of finding eigenvalues and eigenvectors without direct computation?

One advantage is that it can be faster and more efficient, especially for large matrices. It also allows us to find the eigenvectors corresponding to specific eigenvalues, rather than having to compute all of them. Additionally, it can be useful when the matrix A is not available, but its inverse A⁻¹ is.

Can we find eigenvalues and eigenvectors of A⁻¹ without any computation?

No, some form of computation is necessary in order to find eigenvalues and eigenvectors of A⁻¹. However, using methods such as the inverse power iteration algorithm or the Cayley-Hamilton theorem can simplify the computation and make it more efficient.

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