{"title":"Optimal Polynomial-time Compression for Boolean Max CSP","authors":"Bart M.P. Jansen, Michał Włodarczyk","doi":"10.1145/3624704","DOIUrl":null,"url":null,"abstract":"In the Boolean maximum constraint satisfaction problem – Max CSP ( Γ ) – one is given a collection of weighted applications of constraints from a finite constraint language Γ , over a common set of variables, and the goal is to assign Boolean values to the variables so that the total weight of satisfied constraints is maximized. There exists a concise dichotomy theorem providing a criterion on Γ for the problem to be polynomial-time solvable and stating that otherwise it becomes NP-hard. We study the NP-hard cases through the lens of kernelization and provide a complete characterization of Max CSP ( Γ ) with respect to the optimal compression size. Namely, we prove that Max CSP ( Γ ) parameterized by the number of variables n is either polynomial-time solvable, or there exists an integer d ≥ 2 depending on Γ , such that: (1) An instance of Max CSP ( Γ ) can be compressed into an equivalent instance with \\(\\mathcal {O}(n^d\\log n) \\) bits in polynomial time, (2) Max CSP( Γ ) does not admit such a compression to \\(\\mathcal {O}(n^{d-\\varepsilon }) \\) bits unless NP⊆co-NP/poly. Our reductions are based on interpreting constraints as multilinear polynomials combined with the framework of ‘constraint implementations’, formerly used in the context of APX-hardness. As another application of our reductions, we reveal tight connections between optimal running times for solving Max CSP( Γ ) . More precisely, we show that obtaining a running time of the form \\(\\mathcal {O}(2^{(1-\\varepsilon)n}) \\) for particular classes of Max CSP s is as hard as breaching this barrier for Max d - SAT for some d .","PeriodicalId":44045,"journal":{"name":"ACM Transactions on Computation Theory","volume":"118 1","pages":"0"},"PeriodicalIF":0.8000,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Computation Theory","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3624704","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 1
Abstract
In the Boolean maximum constraint satisfaction problem – Max CSP ( Γ ) – one is given a collection of weighted applications of constraints from a finite constraint language Γ , over a common set of variables, and the goal is to assign Boolean values to the variables so that the total weight of satisfied constraints is maximized. There exists a concise dichotomy theorem providing a criterion on Γ for the problem to be polynomial-time solvable and stating that otherwise it becomes NP-hard. We study the NP-hard cases through the lens of kernelization and provide a complete characterization of Max CSP ( Γ ) with respect to the optimal compression size. Namely, we prove that Max CSP ( Γ ) parameterized by the number of variables n is either polynomial-time solvable, or there exists an integer d ≥ 2 depending on Γ , such that: (1) An instance of Max CSP ( Γ ) can be compressed into an equivalent instance with \(\mathcal {O}(n^d\log n) \) bits in polynomial time, (2) Max CSP( Γ ) does not admit such a compression to \(\mathcal {O}(n^{d-\varepsilon }) \) bits unless NP⊆co-NP/poly. Our reductions are based on interpreting constraints as multilinear polynomials combined with the framework of ‘constraint implementations’, formerly used in the context of APX-hardness. As another application of our reductions, we reveal tight connections between optimal running times for solving Max CSP( Γ ) . More precisely, we show that obtaining a running time of the form \(\mathcal {O}(2^{(1-\varepsilon)n}) \) for particular classes of Max CSP s is as hard as breaching this barrier for Max d - SAT for some d .