Eigenvalue and diagonalisation question

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In summary: Eigenvalues are "1" and only one eigenvector is needed (in this case, the x-axis).In summary, the matrix M can be diagonalized and its eigenvalues are 1, with the corresponding eigenvector being {1,0}. The space spanned by the eigenvectors is one-dimensional and the matrix is already in Jordan Normal Form.
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spaghetti3451
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Homework Statement



Find the eigenvalues and eigenvectors of the following matrix: M =
1 1
0 1
Can this matrix be diagonalised?

Homework Equations



The Attempt at a Solution



The characteristic equation is [itex](1 - \lambda)^{2} = 0[/itex] which gives [itex]\lambda = 1[/itex]. Substitute [itex]\lambda = 1[/itex] and eigenvector = {x,y} into the eigenvalue equation gives the two equations x+y = x and y = y. The first equation implies that y = 0. The second equation is redundant. So, x is free to assume any complex value. So, the eigenvalue is 1 and the eigenvector is {1,0}.

I think everything I have done so far is fine. If it isn't, please point out.

The problem starts with the second part: 'Can this matrix be diagonalised?' I know that to diagonalise a matrix is equivalent to changing the basis of the matrix and the eigenvectors. I am not quite sure I get this or the fact that the eigenvectors have to span the ? to accomplish the diagonalisation.

Thanks in advance for any help.
 
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  • #2
hi failexam! :smile:
failexam said:
I know that to diagonalise a matrix is equivalent to changing the basis of the matrix and the eigenvectors. I am not quite sure I get this or the fact that the eigenvectors have to span the ? to accomplish the diagonalisation.

changing the basis doesn't change the behaviour …

the space spanned by the eigenvectors of the original matrix is one-dimensional (the line y = o)

how many dimensions is the space spanned by the eigenvectors of a diagonal matrix? :wink:

(alternatively, if it was diagonalised, what would those diagonal entries be?)
 
  • #3
An n by n matrix is "diagonalizable" if and only if it has n independent eigenvectors. If a matrix is NOT diagonalizable, it can be put in "Jordan Normal Form". And the matrix in this problem already is in Jordan Normal Form!
 

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are mathematical concepts used in linear algebra to describe a special set of vectors that have a special relationship with a given linear transformation. Eigenvalues are scalar values that represent how the eigenvectors are scaled by the linear transformation. Eigenvectors are non-zero vectors that remain in the same direction after being transformed by the linear transformation.

2. How are eigenvalues and eigenvectors calculated?

Eigenvalues and eigenvectors are calculated through a process called diagonalization. This involves finding the determinant of a matrix, solving a characteristic equation, and finding the corresponding eigenvectors. Alternatively, they can also be found using power iteration methods or numerical algorithms.

3. Why is diagonalization important?

Diagonalization is important because it allows for the simplification of complex calculations involving matrices. By diagonalizing a matrix, we can break it down into simpler matrices that are easier to manipulate and analyze. This is especially useful in applications such as quantum mechanics and data analysis.

4. How do eigenvalues and eigenvectors relate to real-world applications?

Eigenvalues and eigenvectors have a wide range of applications in various fields such as physics, engineering, computer science, and economics. They are used in image compression, pattern recognition, network analysis, and many other applications. In physics, they are used to describe the behavior of quantum systems, while in economics, they are used to model market dynamics.

5. Can all matrices be diagonalized?

No, not all matrices can be diagonalized. For a matrix to be diagonalizable, it must have a complete set of linearly independent eigenvectors. If a matrix does not have a complete set of eigenvectors, it cannot be diagonalized. Additionally, only square matrices can be diagonalized.

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