Approximate Hypergraph Coloring under Low-discrepancy and Related Promises
A hypergraph is said to be \(\chi\)-colorable if its vertices can be colored with \(\chi\) colors so that no hyperedge is monochromatic. \(2\)-colorability is a fundamental property (called Property B) of hypergraphs and is extensively studied in combinatorics. Algorithmically, however, given a \(2\...
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description | A hypergraph is said to be \(\chi\)-colorable if its vertices can be colored with \(\chi\) colors so that no hyperedge is monochromatic. \(2\)-colorability is a fundamental property (called Property B) of hypergraphs and is extensively studied in combinatorics. Algorithmically, however, given a \(2\)-colorable \(k\)-uniform hypergraph, it is NP-hard to find a \(2\)-coloring miscoloring fewer than a fraction \(2^{-k+1}\) of hyperedges (which is achieved by a random \(2\)-coloring), and the best algorithms to color the hypergraph properly require \(\approx n^{1-1/k}\) colors, approaching the trivial bound of \(n\) as \(k\) increases. In this work, we study the complexity of approximate hypergraph coloring, for both the maximization (finding a \(2\)-coloring with fewest miscolored edges) and minimization (finding a proper coloring using fewest number of colors) versions, when the input hypergraph is promised to have the following stronger properties than \(2\)-colorability: (A) Low-discrepancy: If the hypergraph has discrepancy \(\ell \ll \sqrt{k}\), we give an algorithm to color the it with \(\approx n^{O(\ell^2/k)}\) colors. However, for the maximization version, we prove NP-hardness of finding a \(2\)-coloring miscoloring a smaller than \(2^{-O(k)}\) (resp. \(k^{-O(k)}\)) fraction of the hyperedges when \(\ell = O(\log k)\) (resp. \(\ell=2\)). Assuming the UGC, we improve the latter hardness factor to \(2^{-O(k)}\) for almost discrepancy-\(1\) hypergraphs. (B) Rainbow colorability: If the hypergraph has a \((k-\ell)\)-coloring such that each hyperedge is polychromatic with all these colors, we give a \(2\)-coloring algorithm that miscolors at most \(k^{-\Omega(k)}\) of the hyperedges when \(\ell \ll \sqrt{k}\), and complement this with a matching UG hardness result showing that when \(\ell =\sqrt{k}\), it is hard to even beat the \(2^{-k+1}\) bound achieved by a random coloring. |
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Algorithmically, however, given a \(2\)-colorable \(k\)-uniform hypergraph, it is NP-hard to find a \(2\)-coloring miscoloring fewer than a fraction \(2^{-k+1}\) of hyperedges (which is achieved by a random \(2\)-coloring), and the best algorithms to color the hypergraph properly require \(\approx n^{1-1/k}\) colors, approaching the trivial bound of \(n\) as \(k\) increases. In this work, we study the complexity of approximate hypergraph coloring, for both the maximization (finding a \(2\)-coloring with fewest miscolored edges) and minimization (finding a proper coloring using fewest number of colors) versions, when the input hypergraph is promised to have the following stronger properties than \(2\)-colorability: (A) Low-discrepancy: If the hypergraph has discrepancy \(\ell \ll \sqrt{k}\), we give an algorithm to color the it with \(\approx n^{O(\ell^2/k)}\) colors. However, for the maximization version, we prove NP-hardness of finding a \(2\)-coloring miscoloring a smaller than \(2^{-O(k)}\) (resp. \(k^{-O(k)}\)) fraction of the hyperedges when \(\ell = O(\log k)\) (resp. \(\ell=2\)). Assuming the UGC, we improve the latter hardness factor to \(2^{-O(k)}\) for almost discrepancy-\(1\) hypergraphs. (B) Rainbow colorability: If the hypergraph has a \((k-\ell)\)-coloring such that each hyperedge is polychromatic with all these colors, we give a \(2\)-coloring algorithm that miscolors at most \(k^{-\Omega(k)}\) of the hyperedges when \(\ell \ll \sqrt{k}\), and complement this with a matching UG hardness result showing that when \(\ell =\sqrt{k}\), it is hard to even beat the \(2^{-k+1}\) bound achieved by a random coloring.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Algorithms ; Apexes ; Color ; Coloring ; Combinatorial analysis ; Graph theory ; Graphs ; Hardness ; Maximization ; Optimization</subject><ispartof>arXiv.org, 2015-06</ispartof><rights>2015. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). 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Algorithmically, however, given a \(2\)-colorable \(k\)-uniform hypergraph, it is NP-hard to find a \(2\)-coloring miscoloring fewer than a fraction \(2^{-k+1}\) of hyperedges (which is achieved by a random \(2\)-coloring), and the best algorithms to color the hypergraph properly require \(\approx n^{1-1/k}\) colors, approaching the trivial bound of \(n\) as \(k\) increases. In this work, we study the complexity of approximate hypergraph coloring, for both the maximization (finding a \(2\)-coloring with fewest miscolored edges) and minimization (finding a proper coloring using fewest number of colors) versions, when the input hypergraph is promised to have the following stronger properties than \(2\)-colorability: (A) Low-discrepancy: If the hypergraph has discrepancy \(\ell \ll \sqrt{k}\), we give an algorithm to color the it with \(\approx n^{O(\ell^2/k)}\) colors. 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(B) Rainbow colorability: If the hypergraph has a \((k-\ell)\)-coloring such that each hyperedge is polychromatic with all these colors, we give a \(2\)-coloring algorithm that miscolors at most \(k^{-\Omega(k)}\) of the hyperedges when \(\ell \ll \sqrt{k}\), and complement this with a matching UG hardness result showing that when \(\ell =\sqrt{k}\), it is hard to even beat the \(2^{-k+1}\) bound achieved by a random coloring.</description><subject>Algorithms</subject><subject>Apexes</subject><subject>Color</subject><subject>Coloring</subject><subject>Combinatorial analysis</subject><subject>Graph theory</subject><subject>Graphs</subject><subject>Hardness</subject><subject>Maximization</subject><subject>Optimization</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqNjUELgjAYhkcQJOV_GHQW1rfUXUMKiQ4R3WW4ZRPd1jel_Pd56Ad0eg_P8_AuSASc7xKxB1iROISWMQZZDmnKI3I-eI_uY3o5aFpOXmOD0j9p4TqHxjZ0tEojvbh3okyoUXtp64lKq-hNd3Ok6BVdb4IOG7J8yC7o-Ldrsj0d70WZzAevUYehat2IdkYVMAFZJnIB_D_rCxApPUo</recordid><startdate>20150622</startdate><enddate>20150622</enddate><creator>Vijay V S P Bhattiprolu</creator><creator>Venkatesan Guruswami</creator><creator>Lee, Euiwoong</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20150622</creationdate><title>Approximate Hypergraph Coloring under Low-discrepancy and Related Promises</title><author>Vijay V S P Bhattiprolu ; Venkatesan Guruswami ; Lee, Euiwoong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_20826687823</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Algorithms</topic><topic>Apexes</topic><topic>Color</topic><topic>Coloring</topic><topic>Combinatorial analysis</topic><topic>Graph theory</topic><topic>Graphs</topic><topic>Hardness</topic><topic>Maximization</topic><topic>Optimization</topic><toplevel>online_resources</toplevel><creatorcontrib>Vijay V S P Bhattiprolu</creatorcontrib><creatorcontrib>Venkatesan Guruswami</creatorcontrib><creatorcontrib>Lee, Euiwoong</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vijay V S P Bhattiprolu</au><au>Venkatesan Guruswami</au><au>Lee, Euiwoong</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Approximate Hypergraph Coloring under Low-discrepancy and Related Promises</atitle><jtitle>arXiv.org</jtitle><date>2015-06-22</date><risdate>2015</risdate><eissn>2331-8422</eissn><abstract>A hypergraph is said to be \(\chi\)-colorable if its vertices can be colored with \(\chi\) colors so that no hyperedge is monochromatic. \(2\)-colorability is a fundamental property (called Property B) of hypergraphs and is extensively studied in combinatorics. Algorithmically, however, given a \(2\)-colorable \(k\)-uniform hypergraph, it is NP-hard to find a \(2\)-coloring miscoloring fewer than a fraction \(2^{-k+1}\) of hyperedges (which is achieved by a random \(2\)-coloring), and the best algorithms to color the hypergraph properly require \(\approx n^{1-1/k}\) colors, approaching the trivial bound of \(n\) as \(k\) increases. In this work, we study the complexity of approximate hypergraph coloring, for both the maximization (finding a \(2\)-coloring with fewest miscolored edges) and minimization (finding a proper coloring using fewest number of colors) versions, when the input hypergraph is promised to have the following stronger properties than \(2\)-colorability: (A) Low-discrepancy: If the hypergraph has discrepancy \(\ell \ll \sqrt{k}\), we give an algorithm to color the it with \(\approx n^{O(\ell^2/k)}\) colors. However, for the maximization version, we prove NP-hardness of finding a \(2\)-coloring miscoloring a smaller than \(2^{-O(k)}\) (resp. \(k^{-O(k)}\)) fraction of the hyperedges when \(\ell = O(\log k)\) (resp. \(\ell=2\)). Assuming the UGC, we improve the latter hardness factor to \(2^{-O(k)}\) for almost discrepancy-\(1\) hypergraphs. (B) Rainbow colorability: If the hypergraph has a \((k-\ell)\)-coloring such that each hyperedge is polychromatic with all these colors, we give a \(2\)-coloring algorithm that miscolors at most \(k^{-\Omega(k)}\) of the hyperedges when \(\ell \ll \sqrt{k}\), and complement this with a matching UG hardness result showing that when \(\ell =\sqrt{k}\), it is hard to even beat the \(2^{-k+1}\) bound achieved by a random coloring.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Apexes Color Coloring Combinatorial analysis Graph theory Graphs Hardness Maximization Optimization |
title | Approximate Hypergraph Coloring under Low-discrepancy and Related Promises |
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