Multilinear Polynomials
For our purposes here, we work over the field . We can now moreover write for the ring of multivariate polynomials over in indeterminates. A polynomial is multilinear if each indeterminate appears in it with individual degree at most 1. We write for the set of multilinears. This thing is -dimensional over (a basis is given by the multilinear monomials ).
Multilinear polynomials and lists of field elements are basically the same thing, as we argue now. We will write for the -dimensional discrete unit cube.
The Equality Indicator Polynomial
For each fixed , the -variate equality indicator polynomial is defined to be:
This polynomial is multilinear. Moreover, its restriction to the cube yields the equality indicator function, the function that, on each pair of bitstrings and , equals 1 if and 0 otherwise.
Multilinear Extensions
Given some multilinear , the evaluation map sends to its evaluations on the cube; that is,
The problem of reversing this mapping is called "multilinear extension". We're going from the list of prescribed values back to the multilinear takes those values.
We argue that is a bijection (multilinear extensions exist and are unique). For dimension-count reasons, it suffices to argue that it's surjective (multilinear extensions exist). Indeed, it's linear over , and both the domain and the codomain are -dimensional. If it's surjective, it must also be injective (multilinear extensions are unique).
We fix a list of prescribed values. We're going to solve this problem using multilinear Lagrange interpolation. Indeed, the polynomial
gives us what we want. Why? First off, is definitely multilinear, since each of the partial specializations is. Moreover, let's pick an arbitrary . Since equals 1 if and only if , the sum winds up with just one nonzero summand, . In other words, holds for each , which is what we wanted to show.
It's worth pointing out that you can show directly that is injective, without relying on dimension-counting or Lagrange interpolation. This proof is fairly interesting too; it's carried out in Thaler [Tha23, Fact 3.5.].
Succinctness
There is one final fact that makes things work. The equality indicator polynomial is succinct, meaning that for each pair of points and in , the evaluation can be computed in time.
To see why this is true, just look at the defining equation of . It's a product, with terms; moreover, each term in the product can be computed using just a few operations. Actually, in characteristic 2, things get even nicer: we get the simplification
since cancels from each term in the product. Thus, we can get away with just total multiplications.