Polarised random -SAT

Joel Larsson Danielsson, Klas Markström
{"title":"Polarised random -SAT","authors":"Joel Larsson Danielsson, Klas Markström","doi":"10.1017/s0963548323000226","DOIUrl":null,"url":null,"abstract":"<p>In this paper we study a variation of the random <span><span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231006153727984-0672:S0963548323000226:S0963548323000226_inline2.png\"><span data-mathjax-type=\"texmath\"><span>$k$</span></span></img></span></span>-SAT problem, called polarised random <span><span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231006153727984-0672:S0963548323000226:S0963548323000226_inline3.png\"><span data-mathjax-type=\"texmath\"><span>$k$</span></span></img></span></span>-SAT, which contains both the classical random <span><span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231006153727984-0672:S0963548323000226:S0963548323000226_inline4.png\"><span data-mathjax-type=\"texmath\"><span>$k$</span></span></img></span></span>-SAT model and the random version of monotone <span><span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231006153727984-0672:S0963548323000226:S0963548323000226_inline5.png\"><span data-mathjax-type=\"texmath\"><span>$k$</span></span></img></span></span>-SAT another well-known NP-complete version of SAT. In this model there is a polarisation parameter <span><span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231006153727984-0672:S0963548323000226:S0963548323000226_inline6.png\"><span data-mathjax-type=\"texmath\"><span>$p$</span></span></img></span></span>, and in half of the clauses each variable occurs negated with probability <span><span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231006153727984-0672:S0963548323000226:S0963548323000226_inline7.png\"><span data-mathjax-type=\"texmath\"><span>$p$</span></span></img></span></span> and pure otherwise, while in the other half the probabilities are interchanged. For <span><span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231006153727984-0672:S0963548323000226:S0963548323000226_inline8.png\"><span data-mathjax-type=\"texmath\"><span>$p=1/2$</span></span></img></span></span> we get the classical random <span><span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231006153727984-0672:S0963548323000226:S0963548323000226_inline9.png\"><span data-mathjax-type=\"texmath\"><span>$k$</span></span></img></span></span>-SAT model, and at the other extreme we have the fully polarised model where <span><span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231006153727984-0672:S0963548323000226:S0963548323000226_inline10.png\"><span data-mathjax-type=\"texmath\"><span>$p=0$</span></span></img></span></span>, or 1. Here there are only two types of clauses: clauses where all <span><span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231006153727984-0672:S0963548323000226:S0963548323000226_inline11.png\"><span data-mathjax-type=\"texmath\"><span>$k$</span></span></img></span></span> variables occur pure, and clauses where all <span><span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231006153727984-0672:S0963548323000226:S0963548323000226_inline12.png\"><span data-mathjax-type=\"texmath\"><span>$k$</span></span></img></span></span> variables occur negated. That is, for <span><span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231006153727984-0672:S0963548323000226:S0963548323000226_inline13.png\"><span data-mathjax-type=\"texmath\"><span>$p=0$</span></span></img></span></span>, and <span><span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231006153727984-0672:S0963548323000226:S0963548323000226_inline14.png\"><span data-mathjax-type=\"texmath\"><span>$p=1$</span></span></img></span></span>, we get an instance of random <span>monotone</span> <span><span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231006153727984-0672:S0963548323000226:S0963548323000226_inline15.png\"><span data-mathjax-type=\"texmath\"><span>$k$</span></span></img></span></span>-SAT.</p><p>We show that the threshold of satisfiability does not decrease as <span><span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231006153727984-0672:S0963548323000226:S0963548323000226_inline16.png\"><span data-mathjax-type=\"texmath\"><span>$p$</span></span></img></span></span> moves away from <span><span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231006153727984-0672:S0963548323000226:S0963548323000226_inline17.png\"><span data-mathjax-type=\"texmath\"><span>$\\frac{1}{2}$</span></span></img></span></span> and thus that the satisfiability threshold for polarised random <span><span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231006153727984-0672:S0963548323000226:S0963548323000226_inline18.png\"><span data-mathjax-type=\"texmath\"><span>$k$</span></span></img></span></span>-SAT with <span><span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231006153727984-0672:S0963548323000226:S0963548323000226_inline19.png\"><span data-mathjax-type=\"texmath\"><span>$p\\neq \\frac{1}{2}$</span></span></img></span></span> is an upper bound on the threshold for random <span><span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231006153727984-0672:S0963548323000226:S0963548323000226_inline20.png\"/><span data-mathjax-type=\"texmath\"><span>$k$</span></span></span></span>-SAT. Hence the satisfiability threshold for random monotone <span><span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231006153727984-0672:S0963548323000226:S0963548323000226_inline21.png\"/><span data-mathjax-type=\"texmath\"><span>$k$</span></span></span></span>-SAT is at least as large as for random <span><span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231006153727984-0672:S0963548323000226:S0963548323000226_inline22.png\"/><span data-mathjax-type=\"texmath\"><span>$k$</span></span></span></span>-SAT, and we conjecture that asymptotically, for a fixed <span><span><img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231006153727984-0672:S0963548323000226:S0963548323000226_inline23.png\"/><span data-mathjax-type=\"texmath\"><span>$k$</span></span></span></span>, the two thresholds coincide.</p>","PeriodicalId":10503,"journal":{"name":"Combinatorics, Probability and Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Combinatorics, Probability and Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/s0963548323000226","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In this paper we study a variation of the random Abstract Image$k$-SAT problem, called polarised random Abstract Image$k$-SAT, which contains both the classical random Abstract Image$k$-SAT model and the random version of monotone Abstract Image$k$-SAT another well-known NP-complete version of SAT. In this model there is a polarisation parameter Abstract Image$p$, and in half of the clauses each variable occurs negated with probability Abstract Image$p$ and pure otherwise, while in the other half the probabilities are interchanged. For Abstract Image$p=1/2$ we get the classical random Abstract Image$k$-SAT model, and at the other extreme we have the fully polarised model where Abstract Image$p=0$, or 1. Here there are only two types of clauses: clauses where all Abstract Image$k$ variables occur pure, and clauses where all Abstract Image$k$ variables occur negated. That is, for Abstract Image$p=0$, and Abstract Image$p=1$, we get an instance of random monotone Abstract Image$k$-SAT.

We show that the threshold of satisfiability does not decrease as Abstract Image$p$ moves away from Abstract Image$\frac{1}{2}$ and thus that the satisfiability threshold for polarised random Abstract Image$k$-SAT with Abstract Image$p\neq \frac{1}{2}$ is an upper bound on the threshold for random Abstract Image$k$-SAT. Hence the satisfiability threshold for random monotone Abstract Image$k$-SAT is at least as large as for random Abstract Image$k$-SAT, and we conjecture that asymptotically, for a fixed Abstract Image$k$, the two thresholds coincide.

偏振随机-SAT
在本文中,我们研究了随机$k$ -SAT问题的一个变体,称为极化随机$k$ -SAT,它既包含经典的随机$k$ -SAT模型,也包含单调的随机版本$k$ -SAT,另一个著名的np完全版本的SAT。在这个模型中,有一个极化参数$p$,在一半的子句中,每个变量都以概率为负$p$和纯否则。而另一半的概率是互换的。对于$p=1/2$,我们得到经典的随机$k$ -SAT模型,而在另一个极端,我们有完全极化的模型,$p=0$,或1。这里只有两种类型的子句:一种是所有$k$变量纯出现的子句,另一种是所有$k$变量为负值的子句。也就是说,对于$p=0$和$p=1$,我们得到一个随机单调的实例$k$ -SAT。我们表明,满意度阈值不会随着$p$远离$\frac{1}{2}$而降低,因此,极化随机$k$ -SAT与$p\neq \frac{1}{2}$的满意度阈值是随机$k$ -SAT阈值的上界。因此,随机单调$k$ -SAT的可满足阈值至少与随机$k$ -SAT一样大,并且我们推测,对于固定的$k$,这两个阈值渐近地重合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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