How Can I Find Eigenvalues and Normalized Eigenvectors for a Matrix?

In summary, the conversation was about finding the eigen values and normalized eigen vectors for a given matrix. The attempt at a solution involved solving for the eigen values, which were found to be λ = cosθ ± isinθ. However, there was uncertainty about how to solve for the eigen vectors, with one possible solution being x = 1 and y = i.
  • #1
debjit625
40
0

Homework Statement


Find the eigen values and normalized eigen vectors for the matrix

cosθ sinθ
-sinθ cosθ

2. The attempt at a solution
Well I did the eigen values hope they are correct but can't solve for eigen vectors

Eigen values are
λ = cosθ ± isinθ

on solving for eigen vector for eigen value λ = cosθ + isinθ ,I got
x + iy = 0 ,hence only solution x = y = 0 which is not the solution I guess (eigen vector can't be null vector)
or could I take y = i and x = 1 then it is solvable but again can I take imaginary numbers ?

Thanks
 
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  • #2
debjit625 said:
x + iy = 0 ,hence only solution x = y = 0

x and y are not necessarily real.
 
  • #3
That means I take y = i and x = 1.
Thanks.
 

Related to How Can I Find Eigenvalues and Normalized Eigenvectors for a Matrix?

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are mathematical concepts used in linear algebra to describe the properties of a linear transformation. Eigenvalues represent the scale factor by which a vector is stretched or compressed when it is transformed, and eigenvectors represent the direction of this transformation.

2. How are eigenvalues and eigenvectors calculated?

Eigenvalues and eigenvectors can be calculated by solving the characteristic equation of a square matrix. This involves finding the roots of the polynomial equation det(A-λI) = 0, where A is the matrix and λ is the eigenvalue.

3. What is the significance of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are important in various fields such as physics, engineering, and data analysis. They provide a way to simplify complex systems and better understand the behavior of transformations.

4. Can a matrix have more than one eigenvalue?

Yes, a matrix can have multiple eigenvalues and corresponding eigenvectors. However, the number of eigenvalues is limited by the dimension of the matrix. For an n x n matrix, there can be at most n distinct eigenvalues.

5. How are eigenvalues and eigenvectors used in data analysis?

Eigenvalues and eigenvectors are commonly used in dimensionality reduction techniques such as principal component analysis (PCA). They allow for the transformation of complex datasets into a lower-dimensional space while preserving important information, making it easier to analyze and visualize the data.

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