Tensor Products and Bases
Since we’re looking at vector spaces, which are special kinds of modules, we know that has a tensor product structure. Let’s see what this means when we pick bases.
First off, let’s remember what the tensor product of two vector spaces and
is. It’s a new vector space
and a bilinear (linear in each of two variables separately) function
satisfying a certain universal property. Specifically, if
is any bilinear function it must factor uniquely through
as
. The catch here is that when we say “linear” and “bilinear” we mean that the functions preserve both addition and scalar multiplication. As with any other universal property, such a tensor product will be uniquely defined up to isomorphism.
So let’s take finite-dimensional vector spaces and
, and bases
of
and
of
. I say that the vector space with basis
, and with the bilinear function
is a tensor product. Here the expression
is just a name for a basis element of the new vector space. Such elements are indexed by the set of pairs
, where
indexes a basis for
and
indexes a basis for
.
First off, what do I mean by the bilinear function ? Just as for linear functions, we can define bilinear functions by defining them on bases. That is, if we have
and
, we get the vector
in our new vector space, with coefficients .
So let’s take a bilinear function and define a linear function
by setting
We can easily check that does indeed factor as desired, since
so on basis elements. By linearity, they must agree for all pairs
. It should also be clear that we can’t define
any other way and hope to satisfy this equation, so the factorization is unique.
Thus if we have bases of
and
of
, we immediately get a basis
of
. As a side note, we immediately see that the dimension of the tensor product of two vector spaces is the product of their dimensions.
