## Tensor Product for Programmers

- By Jim Kennington
- Mon 15 April 2019

The introduction to tensor products and tensor algebras is often riddled with rigor, in which a mathematician would delight but a programmer would despair. I find myself in the intersection of these camps and while I appreciate notation, a simpler introduction is possible using functional programming concepts.

Tensors are defined and introduced in two equivalent ways. The first way, called the "expansion coefficient" (or array) style of introducing tensors relies on many indices and iterates over the n dimensions of some array (n-dimensional generalization of a matrix) Wikipedia (2019). I have found this approach to be overly cluttered; missing the forest for the trees. Instead, I prefer the second way of defining tensors, namely as "multilinear maps" Roman (2007) Jeevanjee (2015). This technique focuses on functions and interfaces, as opposed to components, and will be the chosen method of explaining below.

Before we begin, a brief note about preferences between the "coefficient" and "multilinear" approaches. The latter way has found greater resonance with mathematicians, while programmers often find the former more comforting. I believe this is caused by an over-reliance on data structures as atomic units of understanding. True, it is natural as a developer to ask "but what *is* the object"; however, using more functional-programming style of thought, the multilinear map approach is actually simpler! No need to keep track of various coefficients on various axes of some imaginary n-dimensional array (leave that to `numpy`

). In the below, I outline a functional-programming style analogy for tensors, and the tensor product. Thought the below snippets are in python, some details are left to the imagination (i.e. this code is not a script).

### Setting the Stage

Before we get to define tensors, we need to briefly define a few building blocks. First, there is a field \(C\), commonly the reals \(\mathbb{R}\), occasionally the complex numbers \(\mathbb{C}\). Members of \(C\) are called *scalars* and are represented in the code by the type `scalar`

. Second, there are vector spaces \(V\) over this field, with the usual properties of closure under addition and scalar multiplication. Elements of \(V\) are of type `vector`

. Note, I am not specifying a vector as a tuple of scalars - though that is a valid vector space, there are others based on non-tuple like entities, like the vector space of square-integrable functions! The last piece of machinery is the dual space \(V^*\). If you're not familiar with the dual space, it is essentially the set of all linear functions that take 1 vector and spit out a scalar (specifically \(V^* = \{f: V\rightarrow C\}\) where \(f\) is linear). Elements of \(V^*\) are of type `dual`

.

#### Some dual vectors

Recall that dual vectors are functions that take a vector and return a scalar. Let \(f, g \in V^*\). In code:

```
def f(v: vector) -> scalar:
pass
def g(w: vector) -> scalar:
pass
```

#### Tensor Product

Now let's define the tensor product of \(f\) and \(g\) as \(h = f \otimes g\). What this amounts to, is combining the functional interface into a new, single tensor (function), that curries to the functions it was made from! Specifically:

```
def h(a: vector, b: vector) -> scalar:
return f(a) * g(b)
```

In some sense, the tensor (or outer) product is like a concatenation operation, that joins functions together, using the superset of arguments, and passing those arguments back to the original functions returning a scalar! This definition is easy!

### Bibliography

Nadir Jeevanjee.
*An Introduction to Tensors and Group Theory for Physicists*.
Springer Science+Business Media, New York, NY, 2015.
ISBN 978-3-319-14793-2. ↩

Steven Roman.
*Advanced Linear Algebra*.
Number 135 in Graduate Texts in Mathematics.
Springer, New York, 3rd ed edition, 2007.
ISBN 978-0-387-72828-5. ↩

Wikipedia.
Tensor, as multidimensional arrays.
*Wikipedia*, April 2019. ↩