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# Equal-Subset-Sum Faster Than the Meet-in-the-Middle

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LIPIcs.ESA.2019.73.pdf
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## Acknowledgements

The authors would like to thank anonymous reviewers for their remarks and suggestions. This research has been initiated during Parameterized Algorithms Retreat of University of Warsaw 2019, Karpacz, 25.02-01.03.2019.

## Cite As

Marcin Mucha, Jesper Nederlof, Jakub Pawlewicz, and Karol Węgrzycki. Equal-Subset-Sum Faster Than the Meet-in-the-Middle. In 27th Annual European Symposium on Algorithms (ESA 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 144, pp. 73:1-73:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.ESA.2019.73

## Abstract

In the Equal-Subset-Sum problem, we are given a set S of n integers and the problem is to decide if there exist two disjoint nonempty subsets A,B subseteq S, whose elements sum up to the same value. The problem is NP-complete. The state-of-the-art algorithm runs in O^*(3^(n/2)) <= O^*(1.7321^n) time and is based on the meet-in-the-middle technique. In this paper, we improve upon this algorithm and give O^*(1.7088^n) worst case Monte Carlo algorithm. This answers a question suggested by Woeginger in his inspirational survey. Additionally, we analyse the polynomial space algorithm for Equal-Subset-Sum. A naive polynomial space algorithm for Equal-Subset-Sum runs in O^*(3^n) time. With read-only access to the exponentially many random bits, we show a randomized algorithm running in O^*(2.6817^n) time and polynomial space.

## Subject Classification

##### ACM Subject Classification
• Mathematics of computing → Combinatorial algorithms
##### Keywords
• Equal-Subset-Sum
• Subset-Sum
• meet-in-the-middle
• enumeration technique
• randomized algorithm

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