Using Eigenvectors to produce a Diagonal matrix

In summary: So, the first column of P should be the first eigenvector, and the second column should be the second eigenvector.
  • #1
tomeatworld
51
0

Homework Statement


If A=[{5,3},{-2,-2}], find the eigenvectors of A. Using these eigenvectors as matrix P, find P-1 and thus prove P-1AP is diagonal.


Homework Equations


None

The Attempt at a Solution


So i can get the eigenvectors to be <3,-1> and <1,-2> corresponding to eigenvalues 4 and -1 respecitively. The problem however, is choosing which vector should be the first column of the matrix P. I used <3,-1> as the first column, and didn't find a diagonal matrix. Should I have? if not, how should I choose which is the first row? I don't mind trying one then the other while revising, but if it's three 3x3 matricies and I'm in a exam, trying all posiilities isn't really an option. How should you choose?
 
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  • #2
A= P D P-1

Where P is matrix of eigenvectors and D is matrix of eigenvalues on a diagonal


First you get the eigenvalues, then you get the eigenvectors. Your eigenvectors are wrong. Recheck your work to verify that eigenvectors are <-3,1> and <-1,2>.

Once you do that you can set P=[-3 -1; 1 2], D=[4 0; 0 -1], P-1=[-0.4 -0.2; 0.2 0.6] Do all the work to verify these results.

PDP-1 = [5 3; -2 -2]
 
Last edited:
  • #3
tomeatworld's eigenvectors are correct. It doesn't matter which vector you choose to be the first column of P, but how you choose will affect how the eigenvalues appear in the diagonal matrix D.
 
  • #4
Mark44 said:
tomeatworld's eigenvectors are correct. It doesn't matter which vector you choose to be the first column of P, but how you choose will affect how the eigenvalues appear in the diagonal matrix D.

Ah you right, either eigenvector (+/-)[-3;1] and (+/-)[1;-2] would do for lambda=4 and lambda=-1, respectively
 
  • #5
Emphasis mine:
tomeatworld said:
So i can get the eigenvectors to be <3,-1> and <1,-2> corresponding to eigenvalues 4 and -1 respecitively. The problem however, is choosing which vector should be the first column of the matrix P. I used <3,-1> as the first column, and didn't find a diagonal matrix. Should I have? if not, how should I choose which is the first row?
So, which did you do, make 3,-1 be the first row or the first column? Was your result

[tex]P^{-1}AP = \bmatrix 8.8 & -5.6 \\ 8.4 & -5.4\endbmatrix[/tex]

If so, you constructed your P matrix as

[tex]P=\bmatrix 3 & -1 \\ 1 & -2\endbmatrix[/tex]

It should be

[tex]P=\bmatrix \phantom{-}3 & \phantom{-1}1 \\ -1 & -2\endbmatrix[/tex]

The reason is that eigenvectors are column vectors. You computed them via

[tex]A\vec x = \lambda \vec x[/tex]

Written that way, the eigenvectors of an n×n matrix have to be n×1 vectors: column vectors.
 

Related to Using Eigenvectors to produce a Diagonal matrix

1. What are eigenvectors and how are they used to produce a diagonal matrix?

Eigenvectors are special vectors that remain in the same direction after a linear transformation. This means that when a matrix is multiplied by an eigenvector, the resulting vector is a scalar multiple of the original eigenvector. To produce a diagonal matrix, we can use the eigenvectors of the original matrix to create a new matrix with the eigenvalues on the diagonal and zeros everywhere else.

2. How do eigenvectors help in solving systems of linear equations?

Eigenvectors can be used to simplify systems of linear equations by reducing them to a smaller set of equations. This is because eigenvectors represent the direction in which a matrix transformation has the greatest impact, making them useful in understanding the underlying structure of a system of equations.

3. Can any matrix be transformed into a diagonal matrix using eigenvectors?

Not all matrices can be transformed into a diagonal matrix using eigenvectors. A matrix must have a full set of linearly independent eigenvectors in order for this transformation to be possible. If a matrix has repeated eigenvalues, then the transformation may not produce a diagonal matrix.

4. How are eigenvalues and eigenvectors related to each other?

Eigenvalues and eigenvectors are closely related in that an eigenvector is associated with a specific eigenvalue. The eigenvalue represents the scalar by which the eigenvector is scaled when multiplied by the original matrix. This relationship is expressed in the equation Ax = λx, where A is the original matrix, x is the eigenvector, and λ is the corresponding eigenvalue.

5. What are some real-world applications of using eigenvectors to produce a diagonal matrix?

Eigenvectors and diagonal matrices have various applications in different fields such as physics, engineering, and data analysis. In physics, diagonalization of matrices is used to solve problems in quantum mechanics and optics. In engineering, diagonal matrices are used in structural analysis and control systems. In data analysis, eigenvectors can be used in dimensionality reduction techniques such as principal component analysis. Additionally, diagonal matrices are useful in solving differential equations and finding the dominant behavior of a system.

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