0

I encountered the symbol "$\otimes$" in a Numberphile video about the Dehn Invariant, and I have no clue what it means.

What, in layman terms, does this symbol do?

I'm not all that experienced in maths and often fail to understand the jargon used in the descriptions of what the tensor symbol does and what a "tensor product" is.

Blue
  • 75,673

1 Answers1

2

In mathematics vectors are abstract entities that like the vectors used in physics to represent forces can be summed or multiplied by a scalar (i.e. a number).

A vector space is roughly speaking a set of vectors that contains every sum of any two vectors in it and every multiple of every vector in it.

If we have two vector spaces $V$ and $W$ we can build up a new vector space $V\times W$ considering pairs of vectors: an element in $V\times W$ will be a pair $$ (v,w)\qquad v\in V,\quad w\in W. $$ The tensor product $V\otimes W$ is a different and a bit subtler way to pair vectors in $V$ with vectors in $W$. A basic element in $V\otimes W$ is something denoted $$ v\otimes w. $$ The main difference between pairs and tensors is that while the pairs $$ (\lambda v,w)\qquad (v,\lambda w) $$ give different elements in $V\times W$ there is an actual equality $$ (\lambda v)\otimes w=v\otimes(\lambda w) $$ as elements in $V\otimes W$ (in the above $\lambda$ is any scalar).

The basic reason to introduce tensor products is to reduce, so to speak, the theory of multilinear functions to the theory of linear functions. For instance, there's a characterization of the determinant of a square matrix that uses the language of tensor products.

Andrea Mori
  • 26,969
  • The Dehn invariant lives in the space $\mathbb R\otimes_{\mathbb Z}\mathbb R/(2\pi)$, which is a tensor product of $\mathbb Z$-modules, not of vector spaces. The idea is similar though. – Kalua Jul 15 '19 at 12:40
  • @Kalua, given that $R$-modules, for a general ring $R$, are a natural generalization of vector spaces things aren't indeed that different. In any event, in order to grasp the basic ideas about tensor products starting with vector spaces is undoubtedly better. – Andrea Mori Jul 15 '19 at 13:12
  • @AndreaMori thank you for your answer. I do think that I'll need more of a background in the field in which tensors are used (not from you, just as I may learn about the field eventually through my education) before I can fully understand their operator. – Christian Albina Jul 15 '19 at 20:02