4 transformation matrices to jordan form

In summary, to find the Jordan form and transformation matrices for a given matrix A, you need to find the characteristic and minimal polynomials, use them to find the Jordan form, and then use the chain method to find the transformation matrices. If the matrix has repeated eigenvalues, you may need to find a basis for a larger kernel space in order to use the chain method.
  • #1
nhrock3
415
0
4)
[tex]A=\left(\begin{array}{cc}4 & -4\\1 & 0\end{array}\right)[/tex]
find the jordan form and the transformation matrices P to this jordan
form.
the caracteristic and minimal polinomial is [tex]P(t)=M(t)=(t-2)^{2}[/tex]
so the jordan form is [tex]J_{A}=\left(\begin{array}{cc}2 & 1\\0 & 2\end{array}\right)[/tex].
my prof taught a method of finding the P
he gave an example like if [tex]J_{B}=\left(\begin{array}{cc}0 & 1\\
0 & 0\end{array}\right)[/tex]
then we use chain method ,for the first column [tex]Tv_{1}=0[/tex] for the
second its [tex]Tv_{2}=v_{1}[/tex]
so [tex]v_{1}\in kerT[/tex] [tex]v_{2}\in kerT^{2}[/tex]
i have two question:
regarding the example of the prof [tex]kerT^{2}[/tex] is [tex]R^{2}[/tex] what vector
to pick for [tex]v_{2}[/tex]?
regarding my original example i have for the first coulumn of the
jordan form [tex]Tv_{1}=2v_{1}[/tex] for the second [tex]Tv_{2}=v_{1}+2v_{2}[/tex]
so i can't assosiate v\_1 and v\_2 with kernel of T
i know that i can assosiate v1 with Ker(T-2I)
but then i lose the chain method my prof taught.
what to do?
 
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  • #2
For the first example, you can pick any vector for v2 since KerT^2 = R^2. For the second example, you can still use the chain method. The idea is to find vectors such that they satisfy the equations Tv1 = 2v1 and Tv2 = v1 + 2v2. In other words, you want to find a basis of Ker(T-2I) and Ker(T-2I) + Span{v1}. Once you have found these two vectors, you can form the matrix P by putting them as columns. This matrix P will give you the transformation from A to its Jordan form.
 

Related to 4 transformation matrices to jordan form

1. What is the purpose of transforming a matrix to Jordan form?

The Jordan form of a matrix is a special form that can reveal important information about the matrix, such as its eigenvalues and eigenvectors. This can make it easier to analyze and perform computations on the matrix.

2. How do you find the transformation matrices to Jordan form?

There are a few different methods for finding the transformation matrices to Jordan form, including the diagonalization method, the eigendecomposition method, and the Schur decomposition method. Each method may be more suitable for certain types of matrices.

3. Can any matrix be transformed to Jordan form?

Not all matrices can be transformed to Jordan form. In order for a matrix to have a Jordan form, it must have a complete set of eigenvectors. Matrices with repeated eigenvalues or non-diagonalizable matrices cannot be transformed to Jordan form.

4. How does the Jordan form differ from other matrix forms?

The Jordan form is unique in that it has a block diagonal structure, with each block corresponding to one eigenvalue. It also has a specific form in which each block has a 1 on the superdiagonal and 0s on all other entries. This makes it easier to analyze and work with compared to other matrix forms.

5. What are some applications of the Jordan form?

The Jordan form has various applications in mathematics and science, including in the study of linear systems, differential equations, and quantum mechanics. It is also useful in data analysis and image processing, as it can simplify and reveal patterns in large datasets.

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