Correlation testing for affine invariant properties on double-struck F pn in the high error regime

Abstract

Recently there has been much interest in Gowers uniformity norms from the perspective of theoretical computer science. This is mainly due to the fact that these norms provide a method for testing whether the maximum correlation of a function f: double-struck Fpn ← double-struck F p with polynomials of degree at most d ≤ p is non-negligible, while making only a constant number of queries to the function. This is an instance of correlation testing. In this framework, a fixed test is applied to a function, and the acceptance probability of the test is dependent on the correlation of the function from the property. This is an analog of proximity oblivious testing, a notion coined by Goldreich and Ron, in the high error regime. We study in this work general properties which are affine invariant and which are correlation testable using a constant number of queries. We show that any such property (as long as the field size is not too small) can in fact be tested by the Gowers uniformity test, and hence having correlation with the property is equivalent to having correlation with degree d polynomials for some fixed d. We stress that our result holds also for non-linear properties which are affine invariant. This completely classifies affine invariant properties which are correlation testable. The proof is based on higher-order Fourier analysis, where we establish a new approximate orthogonality for structures defined by linear forms. In particular, this resolves an open problem posed by Gowers and Wolf. Another ingredient is a nontrivial extension of a graph theoretical theorem of Erdos, Lovasz and Spencer to the context of additive number theory. © 2011 ACM.