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Dense Subset Sum may be the hardestPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2016 (English)In: Leibniz International Proceedings in Informatics, LIPIcs, Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing , 2016Conference paper, Published paper (Refereed)
##### Resource type

Text
##### Abstract [en]

##### Place, publisher, year, edition, pages

Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing , 2016.
##### Keyword [en]

Additive combinatorics, Exponential-time algorithm, Homomorphic hashing, Littlewood-offord problem, Subset Sum
##### National Category

Discrete Mathematics
##### Identifiers

URN: urn:nbn:se:kth:diva-188298DOI: 10.4230/LIPIcs.STACS.2016.13Scopus ID: 2-s2.0-84961625723ISBN: 9783959770019 (print)OAI: oai:DiVA.org:kth-188298DiVA: diva2:936276
##### Conference

33rd Symposium on Theoretical Aspects of Computer Science, STACS 2016, 17 February 2016 through 20 February 2016
#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt450",{id:"formSmash:j_idt450",widgetVar:"widget_formSmash_j_idt450",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt462",{id:"formSmash:j_idt462",widgetVar:"widget_formSmash_j_idt462",multiple:true});
##### Funder

Swedish Research Council Formas, 621-2012-4546
##### Note

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∗(2n/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-δ)n)-time algorithm, with some constant δ > 0. Continuing an earlier work [STACS 2015], we study Subset Sum parameterized by the maximum bin size β, defined as the largest number of subsets of the n input integers that yield the same sum. For every ∈ > 0 we give a truly faster algorithm for instances with β ≤ 2(0.5-∈)n, as well as instances with β ≥ 20.661n. Consequently, we also obtain a characterization in terms of the popular density parameter n/log2 t: if all instances of density at least 1.003 admit a truly faster algorithm, then so does every instance. This goes against the current intuition that instances of density 1 are the hardest, and therefore is a step toward answering the open question in the affirmative. Our results stem from a novel combinatorial analysis of mixings of earlier algorithms for Subset Sum and a study of an extremal question in additive combinatorics connected to the problem of Uniquely Decodable Code Pairs in information theory.

QC 20160613

Available from: 2016-06-13 Created: 2016-06-09 Last updated: 2016-06-13Bibliographically approvedCiteExport$(function(){PrimeFaces.cw("TieredMenu","widget_formSmash_lower_j_idt1198",{id:"formSmash:lower:j_idt1198",widgetVar:"widget_formSmash_lower_j_idt1198",autoDisplay:true,overlay:true,my:"left top",at:"left bottom",trigger:"formSmash:lower:exportLink",triggerEvent:"click"});}); $(function(){PrimeFaces.cw("OverlayPanel","widget_formSmash_lower_j_idt1199_j_idt1201",{id:"formSmash:lower:j_idt1199:j_idt1201",widgetVar:"widget_formSmash_lower_j_idt1199_j_idt1201",target:"formSmash:lower:j_idt1199:permLink",showEffect:"blind",hideEffect:"fade",my:"right top",at:"right bottom",showCloseIcon:true});});