Exercise 3.3.11. Suppose that is a subspace with orthogonal complement
, with
a projection matrix onto
and
a projection matrix onto
. What are
and
? Also, show that
is its own inverse.
Answer: Given any vector we have
where
is the projection of
onto
and
is the projection of
onto
. Since
and
are orthogonal complements the sum of the projections
and
is equal to
itself. So we have
and since this is true for all
we have
.
This can be more formally proved as follows: Consider the matrix . It is a projection matrix and it projects onto
. Also, if
is a projection matrix onto
then it is unique. Since
is a projection matrix onto
and
is a projection matrix onto
we therefore have
or
. For the full proof see below.
Since we have
.
(Applying to a vector
first projects
onto
and then projects the resulting vector onto
. But projecting any vector in
onto
will produce the zero vector, since
and
are orthogonal.)
Finally, we have
Since we have
so that
is its own inverse.
Here is the full proof that if a projection matrix onto
and
a projection matrix onto
then
:
We first show that is a projection matrix. The identity matrix
is symmetric, and since
is a projection matrix it is also symmetric. The difference
is therefore symmetric as well, so that we have
.
We also have
Since and
we see that
is a projection matrix.
Onto what subspace does project? For any vector
we have
. Since
is a projection matrix the vector
is in the space onto which
projects, and the vector
is orthogonal to that space. But
projects onto
so
must therefore be in
.
We have thus shown that is a projection matrix that projects onto
. We now show that any such projection matrix is unique.
Suppose that like the matrix
is also a projection matrix onto
. Consider the vector
for any vector
. We have
where we take advantage of the fact that .
But since and
are projection matrices they are symmetric, and therefore their difference
is also symmetric. We thus have
But since and
are projection matrices we have
and
. We thus have
Since is a projection matrix onto
we know that
for any vector
in
. (In other words, when applied to any vector
in
the projection matrix
projects that vector onto itself.) The same is true for
since it is also a projection matrix onto
. We thus have
for all
in
.
Given any vector (not necessarily in
) we then have
since
is a vector in
. Since
is a projection matrix we have
and thus
, so that
for all vectors
. This implies that
.
Similarly for any vector we have
since
is a vector in
. Since
is a projection matrix we have
and thus
, so that
for all vectors
. This implies that
.
Since and
we have
Since we have
, and since this is true for any vector
we must have
or
. A projection matrix
onto a subspace
is therefore unique.
Thus since is a projection matrix onto
and
is also a projection matrix onto
we have
or
.
NOTE: This continues a series of posts containing worked out exercises from the (out of print) book Linear Algebra and Its Applications, Third Edition by Gilbert Strang.
If you find these posts useful I encourage you to also check out the more current Linear Algebra and Its Applications, Fourth Edition, Dr Strang’s introductory textbook Introduction to Linear Algebra, Fifth Edition
and the accompanying free online course, and Dr Strang’s other books
.