Sums and differences of correlated random sets
Abstract
Many fundamental questions in additive number theory (such as Goldbach's conjecture, Fermat's last theorem, and the Twin Primes conjecture) can be expressed in the language of sum and difference sets. As a typical pair of elements contributes one sum and two differences, we expect that for a finite set . However, in 2006 Martin and O'Bryant showed that a positive proportion of subsets of are sum-dominant, and Zhao later showed that this proportion converges to a positive limit as . Related problems, such as constructing explicit families of sum-dominant sets, computing the value of the limiting proportion, and investigating the behavior as the probability of including a given element in to go to zero, have been analyzed extensively. We consider many of these problems in a more general setting. Instead of just one set , we study sums and differences of pairs of \emph{correlated} sets . Specifically, we place each element in with probability , while goes in with probability if and probability if . If , we call the pair a \emph{sum-dominant -pair}. We prove that for any fixed in , is a sum-dominant -pair with positive probability, and show that this probability approaches a limit . Furthermore, we show that the limit function is continuous. We also investigate what happens as decays with , generalizing results of Hegarty-Miller on phase transitions. Finally, we find the smallest sizes of MSTD pairs.
Cite
@article{arxiv.1401.2588,
title = {Sums and differences of correlated random sets},
author = {Thao Do and Archit Kulkarni and Steven J. Miller and David Moon and Jake Wellens},
journal= {arXiv preprint arXiv:1401.2588},
year = {2014}
}
Comments
Version 1.0, 19 pages. Keywords: More Sum Than Difference sets, correlated random variables, phase transition