Find a good configuration of HAPs

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One of the difficulties of thinking about the Erdős discrepancy problem is that one can get hung up on HAPs with small common differences, when ultimately what should matter is the more typical HAPs, which have large common differences. This is less of a problem when one is looking at multiplicative sequences, since then all one cares about is partial sums. However, the strategy to be described on this page concerns a direct attack on the general problem.

The rough idea is this. Recall that a hypergraph is a collection of subsets of some set X. We borrow graph terminology and call the elements of X verticesand the sets in H edges (or sometimes hyperedges if we want to make it clear that our graph is hyper). If H is a hypergraph with vertex set X and f is a function from X to {-1,1}, then the discrepancy of f is the maximum of [math]|\sum_{x\in A}f(x)|[/math] over all edges A of H. The discrepancy of H is the minimum discrepancy of any function [math]f:X\to\{-1,1\}[/math].

The Erdős discrepancy problem is asking us to prove that the discrepancy of the hypergraph whose vertex set is [math]\mathbb{N}[/math] and whose edges are all finite HAPs is infinite. One obvious strategy for doing this is to identify within this hypergraph a collection of hypergraphs [math]H_n[/math] that have properties that allow us to prove arbitrarily good lower bounds for the discrepancy. What makes this a strategy, rather than a trivial reformulation of the problem (after all, one might suggest that there is no point in taking all HAPs) is that one would be aiming to find subhypergraphs of the HAP hypergraph that lent themselves particularly well to certain kinds of combinatorial arguments.

This point will be much clearer if we discuss an actual example of a subhypergraph that might do the trick. (I shall follow the usual practice, when talking informally about asymptotic arguments, of talking about "a subhypergraph" when strictly speaking what I mean is a sequence of larger and larger subhypergraphs. I shall picture a single subhypergraph that is very large.