Endomorphism/Jordan normal form/Introduction/Section
If we consider such a Jordan matrix as a linear mapping on the standard space , then
In particular, is a eigenvector to the eigenvalue . A simple observation shows that there is no further eigenvector linearly independent to (see exercise). The property on the right is equivalent with the condition[2]
for . The eigenvector is a generating element of the kernel of the mapping , and the other standard vectors arise successively as preimages of under .
A square matrix of the form
where each is a Jordan matrix, is called a matrix in
Jordan normal form.The Jordan matrices occurring here are called the Jordan blocks of the matrix. Their eigenvalues might be different or equal. In the matrix
there are three Jordan blocks,
with eigenvalues and again .
We state and prove now the theorem about the Jordan normal form for trigonalizable endomorphisms.
For every trigonalizable endomorphism
on a finite-dimensional -vector space , there exists a basis, such that the describing matrix is in
Jordan normal form.Since is trigonalizable, we can apply fact. Hence, there exists a direct sum decomposition
where the generalized eigenspaces are -invariant. Looking at the situation for each generalized eigenspace, we may assume that has only one eigenvalue , and that
holds. Then,
is nilpotent. Therefore, because of fact, there exists a basis such that is described by a matrix of the form
where the equal or equal . With respect to this basis,
has the form
Hence, every upper triangular matrix is similar to a matrix in Jordan normal form. Over the complex numbers, every matrix can be brought to Jordan normal form. If a matrix has Jordan normal form, then we can read of directly the diagonalizable and the nilpotent part in the sense of
fact:
The diagonal yields the diagonalizable part, and the entries which are strictly above the diagonal, yield the nilpotent part
(in general, this is not true for upper triangular matrices).
We describe how to find to a linear trigonalizable mapping a basis, such that describing matrix with respect to this basis is in Jordan normal form. For this, we determine, for every eigenvalue , the minimal exponent with
This kernel is the generalized eigenspace to . We set
for . This yields the chain
Now, we choose a vector from . The vectors
form a basis for a Jordan block. If this basis generates the generalized eigenspace, then we are done. Otherwise, we look in for another vector which is linearly independent to and to . Again, we add this vector and all its successive images. If is exhausted, then we look whether is already covered, and so on. If the generalized eigenspace to is covered, then we continue with the next eigenvalue.
Under certain circumstances, we can also start with a basis of the eigenspace. If, for example, the eigenspace to is one-dimensional, then we can choose an eigenvector for , and we can find successively preimages under of the vectors, that is, we have to solve the equation
then
etc.
If, for example, the eigenspace is -dimensional and the generalized eigenspace is -dimensional, then we only have to find a preimage for one eigenvector under .
We consider the matrix
and want to bring it to Jordan normal form. bringen. The vector is an eigenvector to the eigenvalue . We have
so that there exists no further linearly independent eigenvector. We look at the linear system . This imples (looking at the second row) and so (we can choose freely to be ). Hence, we set . Finally, we need a solution for . This yields the equation . The matrix acts as
so that the mapping is described with respect to the basis by
This matrix is a Jordan matrix and, in particular, in Jordan normal form.
We consider the matrix
and want to bring it to Jordan normal form. The vectors and are linearly independent eigenvectors to the eigenvalue . We have
so that and span this eigenspace. An eigenvector must be the image of some vector under the matrix . In fact, the linear system
has the solution . Therefore, the matrix acts in the following way
Hence, the mapping is described, with respect to the basis , by the matrix
This matrix is in Jordan normal form with the Jordan blocks and .
We consider the matrix
and want to bring in in Jordan normal for. Here, we have two eigenvalues and, therefore, two two-dimensional generalized eigenspaces, which we treat separately. We have
therefore, belongs to the kernel. The determinant of the upper right submatrix is not , so the rank of the matrix is , and its kernel is one-dimensional. The second power is
a new element of the kernel is . Thus, we have
Because of
we can use the vectors and to establish the first Jordan block.
We have
therefore, belongs to the kernel. The rank of this matrix is again , and the kernel has dimension one. The second power is
a new element in the kernel is . Thus, we have
Because of
we can use the vectors and to establish the second Jordan block. Altogether, the linear mapping defined by has, with respect the basis