The Subset Sum problem asks whether a given set of n positive integers contains a subset of elements that sum up to a given target t. It is an outstanding open question whether the O^*(2^{n/2})-time algorithm for Subset Sum by Horowitz and Sahni [J. ACM 1974] can be beaten in the worst-case setting by a "truly faster", O^*(2^{(0.5-\delta)n})-time algorithm, with some constant \delta > 0. We report some partial progress on this question as outlined in http://arxiv.org/pdf/1508.06019v1.pdf.

Specifically, we study Subset Sum parameterized by the maximum bin size \beta, defined as the largest number of subsets of the n input integers that yield the same sum. For every \epsilon > 0 we give a truly faster algorithm for instances with \beta \leq 2^{(0.5-\epsilon)n}, as well as instances with \beta \geq 2^{0.661n}. Consequently, we also obtain a characterization in terms of the popular density parameter n/\log_2 t: if all instances of density at least 1.003 admit a truly faster algorithm, then so does every instance.

Joint work with Per Austrin, Petteri Kaski and Mikko Koivisto.