This lecture present some useful properties of matrix powers.
Table of contents
We can take non-negative integer powers of a square matrix
by repeatedly multiplying
by itself.
If
is a positive integer and
is a
matrix,
then
We adopt the convention
thatwhere
is the
identity matrix.
Let
be a
matrix and let
be the space of all
column vectors.
Remember that the null
space of
is the
subspace
In other words, the null space is formed by all the vectors of
that are mapped into the zero vector by the linear transformation defined by
.
Here is the first interesting result: the larger a matrix power, the bigger the null space.
Proposition
Let
be a
matrix. Then, the null spaces of the powers of
satisfy
for
any non-negative integer
.
Since
and
is the only vector mapped into the zero vector by the identity transformation,
we
have
For
any non-negative integer
,
let
.
Then,
We
can multiply both sides by
,
so as to
get
which
implies that
.
Thus, all the members of
are also members of
.
In other words,
is included in
.
As we take increasingly greater matrix powers, if at a certain point the growth of null spaces stops, then it stops forever.
Proposition
Let
be a
matrix. If there exist a positive integer
such
that
then
for
any integer
.
We only need to prove that
implies
because then we can generate a chain of equalities so as to obtain
for any
.
We already know
that
We
need to prove inclusion in the other direction. Suppose
.
Then,
or,
equivalently,
Thus,
we have
and,
by the hypothesis that
,
we also have
The
latter fact means
that
or
In
other
words,
Thus,
all the members of
are also members of
.
Hence,
is included in
,
which is what we needed to prove.
The null spaces do not grow indefinitely as we take larger powers, but they eventually stabilize.
Proposition
Let
be a
matrix.
Then,
The proof is by contradiction. Suppose
thatBy
the previous proposition this implies
that
for
all integers
(otherwise we would have
).
Therefore,
where
denotes strict inclusion (i.e., each null space has at least one more member
than the null space immediately preceding it). Since null spaces are
subspaces, having at least one more member is the same as having at least an
additional dimension. As a consequence, the dimension of
must be at least equal to
.
This is impossible because
is a subspace of the space of
vectors (hence
can have at most
dimensions). Thus, the initial hypothesis has led us to a contradiction. As a
consequence, we must have
By combining the previous two propositions, we
obtainfor
any integer
.
Note that the last proposition does not imply that the null spaces stabilize
exactly at
,
as it may well happen
that
for
.
However, the proposition guarantees that the stabilization point is not beyond
.
Let
be the space of all
column vectors. Remember that the column space of a
matrix
,
which coincides with the
range of the linear
transformation defined by the matrix,
is
By the rank-nullity
theorem, for any matrix power
,
we
have
where
and
denote the dimensions of the column space and the null space respectively.
We have demonstrated above that
increases as we increase the power
,
up to
.
As a consequence,
must decrease as we increase
,
up to
.
In other words, the behavior of column spaces is opposite to that of null spaces. The next sections discuss this point in more detail.
The larger a matrix power, the smaller its column space.
Proposition
Let
be the space of all
column vectors. Let
be a
matrix. Then, the column spaces of the powers of
satisfy
for
any non-negative integer
.
Since
,
for any
.
Therefore,
For
any non-negative integer
,
let
.
Then, by the definition of column space, there exists
such that
which
can be written equivalently
as
which
implies that
.
Thus, all the members of
are also members of
.
In other words,
includes
.
As we take increasingly greater matrix powers, if at a certain point the column spaces stop shrinking, then they will never shrink again.
Proposition
Let
be a
matrix. If there exist a positive integer
such
that
then
for
any integer
.
We only need to prove that
implies
because then we can generate a chain of equalities so as to obtain
for any
.
We already know
that
We
need to prove inclusion in the other direction. Let
be the space of all
column vectors. Take any
and define
By
definition,
.
The hypothesis that
implies that
.
Thus, there exists
such
that
or
By
putting (1) and (2) together, we
get
Thus,
we have just proved that, for any
,
there exists
such that (3) holds. Now, suppose
.
Then, there exists
such
that
or
By
(3), there exists
,
such
that
In
other words,
.
Thus, all the members of
are also members of
.
In other words,
The column spaces do not grow indefinitely as we take larger powers, but they eventually stabilize.
Proposition
Let
be a
matrix.
Then,
The proof is by contradiction. Suppose
thatBy
the previous proposition this implies
that
for
all integers
(otherwise we would have
).
Therefore,
where
is the space of all
column vectors and
denotes strict inclusion. In other words, each column space has at least one
less member than the column space immediately preceding it. Since column
spaces are subspaces, having at least one less member is the same as having at
least one less dimension. Since the dimension of
is
,
the dimension of
must be less than
.
This is impossible because the dimension cannot be a negative number. Thus,
the initial hypothesis has led us to a contradiction. As a consequence, we
must have
By combining the previous two propositions, we
obtainfor
any integer
.
A comment similar to those made for null spaces is in order: the last
proposition does not imply that the columns spaces stabilize exactly at
,
as it may well happen
that
for
.
However, the proposition guarantees that the stabilization point is not beyond
.
By putting together the rank-nullity theorem
and the propositions above, we see that, when we increase the power
by one unit, then there are two mutually exclusive possibilities:
the dimension of the null space
increases by at least one unit and the dimension of the row space
decreases by the same amount;
the dimensions of
and
remain unchanged (and they never change again when we further increase
).
In particular, the column and null spaces must stabilize at the same point:
there exists a unique integer
such
that
and
for
any integer
.
Please cite as:
Taboga, Marco (2021). "Matrix power", Lectures on matrix algebra. https://www.statlect.com/matrix-algebra/matrix-power.
Most of the learning materials found on this website are now available in a traditional textbook format.