Selective algorithm processing of subset sum distributions

Fuente: arXiv
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Main Author: Dawes, Nick
Format: Preprint
Published: 2024
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author Dawes, Nick
author_facet Dawes, Nick
contents The efficiency of exact subset sum problem algorithms which compute individual subset sums is defined as $e=min(T/z, 1)$, where $z$ is the number of subset sums computed. $e$ is related to these algorithms' computational complexity. This system maps the sums into $kn$ bins to select its most efficient algorithm for each bin for each input value. These algorithms include additive, subtractive and repeated value dynamic programming. Cases which would otherwise be processed inefficiently (eg: all even values) are handled by modular arithmetic and by dynamically partioning the input values. The system's experimentally validated efficiency corresponds to O(max($T$, $n^2$)) with space complexity O(max($T$, $n$)), for $k=2$.
format Preprint
id arxiv_https___arxiv_org_abs_2409_11076
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Selective algorithm processing of subset sum distributions
Dawes, Nick
Data Structures and Algorithms
F.2.1
The efficiency of exact subset sum problem algorithms which compute individual subset sums is defined as $e=min(T/z, 1)$, where $z$ is the number of subset sums computed. $e$ is related to these algorithms' computational complexity. This system maps the sums into $kn$ bins to select its most efficient algorithm for each bin for each input value. These algorithms include additive, subtractive and repeated value dynamic programming. Cases which would otherwise be processed inefficiently (eg: all even values) are handled by modular arithmetic and by dynamically partioning the input values. The system's experimentally validated efficiency corresponds to O(max($T$, $n^2$)) with space complexity O(max($T$, $n$)), for $k=2$.
title Selective algorithm processing of subset sum distributions
topic Data Structures and Algorithms
F.2.1
url https://arxiv.org/abs/2409.11076