Linear Algebra - diagonalizable matrix

In summary, in order for the matrix A to be diagonalizable, we need 3 linearly independent eigenvectors with 3 distinct eigenvalues. By finding the eigenvalues of A, we see that the values of 3 and 5 are present. However, it is not possible to find a third distinct eigenvalue for any value of t. Therefore, A is not diagonalizable for any value of t.
  • #1
PirateFan308
94
0

Homework Statement


For the following matrix, find the value of t, if any, so that the following matrix is diagonalizable

[tex]A=
\begin{pmatrix}
5 & -2 & 4\\
0 & 3 & t\\
0 & 0 & 5
\end{pmatrix}
[/tex]


The Attempt at a Solution


In order for A to be diagonalizable, we need 3 linearly independent eigenvectors, that is, 3 linearly independent eigenvalues

[tex]det(A-xI)=det
\begin{pmatrix}
5-x & -2 & 4\\
0 & 3-x & t\\
0 & 0 & 5-x
\end{pmatrix}
[/tex]
[tex]=(5-x)det
\begin{pmatrix}
3-x & t\\
0 & 5-x
\end{pmatrix}
[/tex]
[tex]= (5-x)((3-x)(5-x)-0t)[/tex]

The eigenvalues are 3 and 5.
Obviously, it doesn't matter what t is, we will not be able to get the matrix A to be diagonalizable.

My professor said that he thought there was one correct value for t (but he wasn't sure). Is what I've done correct?
 
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  • #2
PirateFan308 said:

Homework Statement


For the following matrix, find the value of t, if any, so that the following matrix is diagonalizable

[tex]A=
\begin{pmatrix}
5 & -2 & 4\\
0 & 3 & t\\
0 & 0 & 5
\end{pmatrix}
[/tex]


The Attempt at a Solution


In order for A to be diagonalizable, we need 3 linearly independent eigenvectors, that is, 3 linearly independent eigenvalues

[tex]det(A-xI)=det
\begin{pmatrix}
5-x & -2 & 4\\
0 & 3-x & t\\
0 & 0 & 5-x
\end{pmatrix}
[/tex]
[tex]=(5-x)det
\begin{pmatrix}
3-x & t\\
0 & 5-x
\end{pmatrix}
[/tex]
[tex]= (5-x)((3-x)(5-x)-0t)[/tex]

The eigenvalues are 3 and 5.
Obviously, it doesn't matter what t is, we will not be able to get the matrix A to be diagonalizable.

My professor said that he thought there was one correct value for t (but he wasn't sure). Is what I've done correct?

It is not a matter of whether or not you have a repeated eigenvalue; what is important is whether the geometric and algebraic multiplicities of an eigenvalue are the same. In other words, the dimensionality of the eigenspace for eigenvalue λ=5 needs to be determined. If λ=5 has two linearly-independent eigenvectors, then the matrix will be diagonalizable, so you need to find eigenvectors.

RGV
 
  • #3
There is a value of t for which the matrix is diagonalizable.
 
  • #4
If [itex]\begin{pmatrix}x \\ y \\ z\end{pmatrix}[/tex] is an eigenvector for A, with eigenvalue 5, then
[tex]\begin{pmatrix}5 & -2 & 4 \\ 0 & 3 & t \\ 0 & 0 & 5\end{pmatrix}\begin{pmatrix}x \\ y \\ z\end{pmatrix}= 5\begin{pmatrix}x \\ y \\ z\end{pmatrix}[/tex]
[tex]\begin{pmatrix}5x- 2y+ 4z \\ 3y+ tz \\ 5z\end{pmatrix}= \begin{pmatrix}5x \\ 5y \\ 5z\end{pmatrix}[/tex]

and so must satify the equations 5x- 2y+ z= 5x, 3y+ tz= 5y, 5z= 5z.

Note that the "5x" terms cancel in the first equation and then there is NO "x" in the equations. One eigenvector with eigenvalue is <1, 0, 0>. There is a single value of t that makes the first two equations identical, giving more than one dimension for the eigenspace.
 
  • #5
HallsofIvy said:
If [tex]\begin{pmatrix}x \\ y \\ z\end{pmatrix}[/tex] is an eigenvector for A, with eigenvalue 5, then
[tex]\begin{pmatrix}5 & -2 & 4 \\ 0 & 3 & t \\ 0 & 0 & 5\end{pmatrix}\begin{pmatrix}x \\ y \\ z\end{pmatrix}= 5\begin{pmatrix}x \\ y \\ z\end{pmatrix}[/tex]
[tex]\begin{pmatrix}5x- 2y+ 4z \\ 3y+ tz \\ 5z\end{pmatrix}= \begin{pmatrix}5x \\ 5y \\ 5z\end{pmatrix}[/tex]

and so must satify the equations 5x- 2y+ z= 5x, 3y+ tz= 5y, 5z= 5z.

Note that the "5x" terms cancel in the first equation and then there is NO "x" in the equations. One eigenvector with eigenvalue is <1, 0, 0>. There is a single value of t that makes the first two equations identical, giving more than one dimension for the eigenspace.

So for eigenvalue 3, if [tex]\begin{pmatrix}x' \\ y' \\ z'\end{pmatrix}[/tex] is also an eigenvector for A

[tex]\begin{pmatrix} 5&-2&4\\0&3&t\\0&0&5\end{pmatrix}\begin{pmatrix}x'\\y'\\z'\end{pmatrix} = 3\begin{pmatrix}x'\\y'\\z'\end{pmatrix}[/tex]

Where there are no free variables, no matter what t is, and [tex]5x'-2y'+4z'=3x'[/tex] and [tex]3y'+tz'=3z'[/tex] and [tex]5z'=3z'[/tex] means that [tex] x'=y'=z'=0[/tex] correct?

So if t=4, the matrix A will always be diagonalizable?

We can say that the 3 linearly independent eigenvectors are (if for the second eigenvector, we take z=1 and x=1 and for the third eigenvector we take z=2 and x=2 because both are free):
[tex]\begin{pmatrix} 0 \\ 0 \\ 0 \end{pmatrix} ~and~~ \begin{pmatrix}1 \\ 2 \\ 1 \end{pmatrix} ~and~~ \begin{pmatrix}1 \\ 4 \\ 2 \end{pmatrix}[/tex]

But there are infinitely many eigenvectors, I just chose these 3. Is this correct?
 
  • #6
But i just realized that the eigenvector [tex]\begin{pmatrix} 0\\0\\0 \end{pmatrix}[/tex]
isn't a proper eigenvector because it is all 0. So I could take the eigenvectors (if for the first eigenvector, we take z=3 and x=1):
[tex]\begin{pmatrix} 1 \\ 6 \\ 3 \end{pmatrix} ~and~~ \begin{pmatrix}1 \\ 2 \\ 1 \end{pmatrix} ~and~~ \begin{pmatrix}1 \\ 4 \\ 2 \end{pmatrix}[/tex]

Is this now correct?
 

1. What is a diagonalizable matrix?

A diagonalizable matrix is a square matrix that can be transformed into a diagonal matrix through a similarity transformation. This means that the matrix can be expressed as a product of three matrices: A = PDP^-1, where P is a matrix of eigenvectors and D is a diagonal matrix of eigenvalues.

2. How do you determine if a matrix is diagonalizable?

A matrix is diagonalizable if it has n linearly independent eigenvectors, where n is the dimension of the matrix. This means that the geometric multiplicity of each eigenvalue must be equal to its algebraic multiplicity. In other words, there must be enough eigenvectors to span the entire vector space.

3. What are the benefits of working with diagonalizable matrices?

Diagonalizable matrices have several benefits in linear algebra. They are easier to work with because they are in diagonal form, making computations such as matrix multiplication and finding powers of the matrix more straightforward. Additionally, diagonalizable matrices have special properties that can be used to solve systems of linear equations and study the behavior of dynamical systems.

4. Can a non-square matrix be diagonalizable?

No, a non-square matrix cannot be diagonalizable. Diagonalization is only defined for square matrices, where the number of rows is equal to the number of columns. Non-square matrices do not have eigenvectors or eigenvalues, which are necessary for diagonalization.

5. How is diagonalization used in real-world applications?

Diagonalizable matrices have numerous applications in real-world problems, such as in physics, engineering, and computer science. They are used to model and analyze systems that exhibit exponential growth or decay, such as populations, radioactive decay, and chemical reactions. Diagonalization is also used in machine learning algorithms and data compression techniques.

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