The Takeoff Towards Optimal Sorting Networks
Martin Marinov, David Gregg

TL;DR
This paper introduces a practical method to find minimal representative filters of depth three for optimal sorting networks, extending previous work on filters of depths one and two, and empirically computes these for inputs less than 17.
Contribution
It presents a new approach combining theory and practice to identify complete sets of filters of depth three for sorting networks, which was previously unaddressed.
Findings
Successfully computed complete sets of filters for n<17
Extended the known filters to include depth three networks
Enhanced the algorithm to identify representatives up to permutation and reflection
Abstract
A complete set of filters for the optimal-depth -input sorting network problem is such that if there exists an -input sorting network of depth then there exists one of the form for some . Previous work on the topic presents a method for finding complete set of filters and that consists only of networks of depths one and two respectively, whose outputs are minimal and representative up to permutation and reflection. Our main contribution is a practical approach for finding a complete set of filters containing only networks of depth three whose outputs are minimal and representative up to permutation and reflection. In previous work, we have developed a highly efficient algorithm for finding extremal sets ( i.e. outputs of comparator networks; itemsets; ) up to permutation. In this paper we present a modification to…
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Taxonomy
TopicsData Mining Algorithms and Applications · Advanced Graph Theory Research · Complexity and Algorithms in Graphs
