Linear Algebra/Jordan Canonical Form
This subsection moves from the canonical form for nilpotent matrices to the one for all matrices.
We have shown that if a map is nilpotent then all of its eigenvalues are zero. We can now prove the converse.
We have a canonical form for nilpotent matrices, that is, for each matrix whose single eigenvalue is zero: each such matrix is similar to one that is all zeroes except for blocks of subdiagonal ones. (To make this representation unique we can fix some arrangement of the blocks, say, from longest to shortest.) We next extend this to all single-eigenvalue matrices.
Observe that if 's only eigenvalue is then 's only eigenvalue is because if and only if . The natural way to extend the results for nilpotent matrices is to represent in the canonical form , and try to use that to get a simple representation for . The next result says that this try works.
Template:AnchorAn array that is all zeroes, except for some number down the diagonal and blocks of subdiagonal ones, is a Jordan block. We have shown that Jordan block matrices are canonical representatives of the similarity classes of single-eigenvalue matrices.
We will now finish the program of this chapter by extending this work to cover maps and matrices with multiple eigenvalues. The best possibility for general maps and matrices would be if we could break them into a part involving their first eigenvalue (which we represent using its Jordan block), a part with , etc.
This ideal is in fact what happens. For any transformation , we shall break the space into the direct sum of a part on which is nilpotent, plus a part on which is nilpotent, etc. More precisely, we shall take three steps to get to this section's major theorem and the third step shows that where are 's eigenvalues.
Suppose that is a linear transformation. Note that the restriction[1] of to a subspace need not be a linear transformation on because there may be an with . To ensure that the restriction of a transformation to a "part" of a space is a transformation on the partwe need the next condition.
Two examples are that the generalized null space and the generalized range space of any transformation are invariant. For the generalized null space, if then where is the dimension of the underlying space and so because is zero also. For the generalized range space, if then for some and then shows that is also a member of .
Thus the spaces and are invariant. Observe also that is nilpotent on because, simply, if has the property that some power of maps it to zero— that is, if it is in the generalized null space— then some power of maps it to zero. The generalized null space is a "part" of the space on which the action of is easy to understand.
The next result is the first of our three steps. It establishes that leaves 's part unchanged.
The second step of the three that we will take to prove this section's major result makes use of an additional property of and , that they are complementary. Recall that if a space is the direct sum of two others then any vector in the space breaks into two parts where and , and recall also that if and are bases for and then the concatenation is linearly independent (and so the two parts of do not "overlap"). The next result says that for any subspaces and that are complementary as well as invariant, the action of on breaks into the "non-overlapping" actions of on and on .
To see that has been decomposed into its action on the parts, observe that the restrictions of to the subspaces and are represented, with respect to the obvious bases, by the matrices and . So, with subspaces that are invariant and complementary, we can split the problem of examining a linear transformation into two lower-dimensional subproblems. The next result illustrates this decomposition into blocks.
From Lemma 2.9 we conclude that if two subspaces are complementary and invariant then is nonsingular if and only if its restrictions to both subspaces are nonsingular.
Now for the promised third, final, step to the main result.
Our major result just translates those steps into matrix terms.
Template:AnchorJordan form is a canonical form for similarity classes of square matrices, provided that we make it unique by arranging the Jordan blocks from least eigenvalue to greatest and then arranging the subdiagonal blocks inside each Jordan block from longest to shortest.
We close with the statement that the subjects considered earlier in this Chpater are indeed, in this sense, exhaustive.
Exercises
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Footnotes
- ↑ More information on restrictions of functions is in the appendix.