Non-Abelian homomorphism testing, and distributions close to their self-convolutions

Michael Ben-or, Don Coppersmith, Mike Luby, Ronitt Rubinfeld

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we study two questions related to the problem of testing whether a function is close to a homomorphism. For two finite groups G, H (not necessarily Abelian), an arbitrary map f : G → H, and a parameter 0 < ε < 1, say that f is ε-close to a homomorphism if there is some homomorphism g such that g and/differ on at most ε|G| elements of G, and say that/is ε-far otherwise. For a given f and ε, a homomorphism tester should distinguish whether f is a homomorphism, or if f is ε-far from a homomorphism. When G is Abelian, it was known that the test which picks 0(1/ε) random pairs x,y and tests that f(x) + f(y) = f(x + y) gives a homomorphism tester. Our first result shows that such a test works for all groups G. Next, we consider functions that are close to their self-convolutions. Let A = {ag|g ε G) be a distribution on G. The self-convolution of A, A′ = {a′g|g ε G], is defined by (Equation Presented) It is known that A = A′ exactly when A is the uniform distribution over a subgroup of G. We show that there is a sense in which this characterization is robust - that is, if A is close in statistical distance to A′, then A must be close to uniform over some subgroup of G. Finally, we show a relationship between the question of testing whether a function is close to a homomorphism via the above test and the question of characterizing functions that are close to their self-convolutions.

Original languageEnglish
Pages (from-to)49-70
Number of pages22
JournalRandom Structures and Algorithms
Volume32
Issue number1
DOIs
StatePublished - Jan 2008
Externally publishedYes

Keywords

  • Convolutions of distributions
  • Linearity testing
  • Sublinear time algorithms

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