4 research outputs found
{SETH}-Based Lower Bounds for Subset Sum and Bicriteria Path
Subset-Sum and k-SAT are two of the most extensively studied problems in computer science, and conjectures about their hardness are among the cornerstones of fine-grained complexity. One of the most intriguing open problems in this area is to base the hardness of one of these problems on the other. Our main result is a tight reduction from k-SAT to Subset-Sum on dense instances, proving that Bellman's 1962 pseudo-polynomial -time algorithm for Subset-Sum on numbers and target cannot be improved to time for any , unless the Strong Exponential Time Hypothesis (SETH) fails. This is one of the strongest known connections between any two of the core problems of fine-grained complexity. As a corollary, we prove a "Direct-OR" theorem for Subset-Sum under SETH, offering a new tool for proving conditional lower bounds: It is now possible to assume that deciding whether one out of given instances of Subset-Sum is a YES instance requires time . As an application of this corollary, we prove a tight SETH-based lower bound for the classical Bicriteria s,t-Path problem, which is extensively studied in Operations Research. We separate its complexity from that of Subset-Sum: On graphs with edges and edge lengths bounded by , we show that the pseudo-polynomial time algorithm by Joksch from 1966 cannot be improved to , in contrast to a recent improvement for Subset Sum (Bringmann, SODA 2017)