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Optimal-depth sorting networks

Authors: Daniel Bundala; Michael Codish; Luís Cruz-Filipe; Peter Schneider-Kamp; Jakub Závodný;

Optimal-depth sorting networks

Abstract

We solve a 40-year-old open problem on the depth optimality of sorting networks. In 1973, Donald E. Knuth detailed, in Volume 3 of "The Art of Computer Programming", sorting networks of the smallest depth known at the time for n =< 16 inputs, quoting optimality for n =< 8. In 1989, Parberry proved the optimality of the networks with 9 =< n =< 10 inputs. In this article, we present a general technique for obtaining such optimality results, and use it to prove the optimality of the remaining open cases of 11 =< n =< 16 inputs. We show how to exploit symmetry to construct a small set of two-layer networks on n inputs such that if there is a sorting network on n inputs of a given depth, then there is one whose first layers are in this set. For each network in the resulting set, we construct a propositional formula whose satisfiability is necessary for the existence of a sorting network of a given depth. Using an off-the-shelf SAT solver we show that the sorting networks listed by Knuth are optimal. For n =< 10 inputs, our algorithm is orders of magnitude faster than the prior ones.

Country
Denmark
Keywords

FOS: Computer and information sciences, sorting networks, SAT solving, Discrete Mathematics (cs.DM), Analysis of algorithms and problem complexity, symmetry breaking, Computer Science - Data Structures and Algorithms, Data Structures and Algorithms (cs.DS), Searching and sorting, Computer Science - Discrete Mathematics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Average
Top 10%
Top 10%
Green
hybrid