commuting matrices
We consider the properties of commuting matrices and linear transformations over a vector space
V.
Two linear transformations φi:V→V, i=1,2 are said to commute if for every v∈V,
φ1(φ2(v))=φ2(φ1(v)). |
If V has finite dimension n and we fix a basis of V then we may represent the linear transformations as n×n matrices Ai and here the condition of commuting linear transformations is equivalent
to testing if their corresponding matrices commute:
A1A2=A2A1. |
Simultaneous triangularisation of commuting matrices over any field can be achieved but may require an extension of the field. The reason begins to be apparent from the study of eigenvalues
.
Remark 1.
Because the implication of commuting matrices is best expressed through eigenvectors
, we prefer the treatment of linear transformations for the .
Proposition 2.
If {φ}i∈I are commuting linear transformations and E is
an eigenspace of φi0 for some i0∈I, then for all i∈I,
φi(E)≤E.
Proof.
Let λ be the eigenvalue of φi0 on E. Take any i∈I and v∈E. Then
φi0(φi(v))=φi(φi0(v))=φi(λv)=λφi(v). |
Therefore φi(v)∈E as E is the λ eigenspace of φi0. In particular, φi(E)≤E. ∎
We have just shown that commuting linear transformations preserve each other’s eigenspaces. This property does not depend on a finite dimension for V or a finite set of commuting transformations. However, to characterize commuting linear transformations further will require that V have finite dimension.
Proposition 3.
Let V be a finite dimensional vector space and let
{φ}i∈I be a family of commuting diagonalizable
linear transformations from V to V. Then φi
can be simultaneously diagonalized.
Proof.
If a finite dimensional linear transformation is diagonalizable over its field then it has all its eigenvalues in the field (under some basis the matrix is diagonal and the eigenvalues are simply those elements on the diagonal.)
If all the eigenvalues of a linear transformation are the same then
the associated diagonal matrix is scalar. If all φi
are scalar then they are simultaneously diagonalized.
Now presume that each φi is not a scalar transformation. Hence there are at least two distinct eigenspaces. It follows each eigenspace of φi has dimension less than that of V.
Now we set up an induction on the dimension of V. When the
dimension of V is 1, all linear transformations are scalar.
Now suppose that for all vector spaces of dimension n, any commuting
diagonalizable linear transformations can be simultaneously
diagonalized. Then in the case where , either all the
linear transformations are scalar and so simultaneously
diagonalized, or at least one is not scalar in which case its eigenspaces
are proper subspaces. Since the maps commute they respect each others eigenspaces. So we restrict the maps to any eigenspace
and by induction simultaneously diagonalize on this subspace.
As the linear transformations are diagonalizable, the sum of the eigenspaces of any is so this process simultaneously diagonalizes each of the
.
∎
Of course it is possible to have commuting matrices which are not diagonalizable. At the other extreme are unipotent matrices, that is, matrices with all eigenvalues 1. Aside from the identity matrix, unipotent matrices are never diagonal. Yet they often commute. But here the generalized eigenspaces
substitute for the usual eigenspaces.
It is generally not true that two unipotent matrices commute, even if they share the same eigenspace. For example, the set of unitriangular matrices forms a nilpotent group which is abelian
only for -matrices.
However, if we consider unipotent matrices of the form
we find these to correspond to matrices under addition. Thus this large family of unipotent matrices do commute.
Title | commuting matrices |
---|---|
Canonical name | CommutingMatrices |
Date of creation | 2013-03-22 15:54:12 |
Last modified on | 2013-03-22 15:54:12 |
Owner | Algeboy (12884) |
Last modified by | Algeboy (12884) |
Numerical id | 20 |
Author | Algeboy (12884) |
Entry type | Definition |
Classification | msc 15A04 |
Related topic | SimultaneousTriangularisationOfCommutingMatricesOverAnyField2 |
Related topic | CommonEigenvectorOfADiagonalElementCrossSection |