Additive Sparsification of CSPs

IF 0.9 3区 计算机科学 Q3 COMPUTER SCIENCE, THEORY & METHODS
Eden Pelleg, Stanislav Živný
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引用次数: 1

Abstract

Multiplicative cut sparsifiers, introduced by Benczúr and Karger [STOC’96], have proved extremely influential and found various applications. Precise characterisations were established for sparsifiability of graphs with other 2-variable predicates on Boolean domains by Filtser and Krauthgamer [SIDMA’17] and non-Boolean domains by Butti and Živný [SIDMA’20]. Bansal, Svensson and Trevisan [FOCS’19] introduced a weaker notion of sparsification termed “additive sparsification”, which does not require weights on the edges of the graph. In particular, Bansal et al. designed algorithms for additive sparsifiers for cuts in graphs and hypergraphs. As our main result, we establish that all Boolean Constraint Satisfaction Problems (CSPs) admit an additive sparsifier; that is, for every Boolean predicate P :{ 0,1} k → { 0,1} of a fixed arity k , we show that CSP( P ) admits an additive sparsifier. Under our newly introduced notion of all-but-one sparsification for non-Boolean predicates, we show that CSP( P ) admits an additive sparsifier for any predicate P : D k → { 0,1} of a fixed arity k on an arbitrary finite domain D .
csp的加性稀疏化
由Benczúr和Karger [STOC ' 96]引入的乘法切割稀疏剂已被证明极具影响力,并找到了各种应用。Filtser和Krauthgamer [SIDMA ' 17]以及Butti和Živný [SIDMA ' 20]在布尔域和非布尔域建立了具有其他2变量谓词的图的稀疏性的精确表征。Bansal, Svensson和Trevisan [FOCS ' 19]引入了一种较弱的稀疏化概念,称为“加性稀疏化”,它不需要在图的边缘上设置权重。特别是,Bansal等人设计了用于图和超图切割的加性稀疏化算法。作为我们的主要结果,我们建立了所有布尔约束满足问题(csp)都允许一个加性稀疏子;即,对于每一个布尔谓词P:{0,1} k→{0,1},我们证明了CSP(P)允许一个加性稀疏子。在我们新引入的非布尔谓词的除一以外的全部稀疏化的概念下,我们证明了CSP(P)允许在任意有限域D上对任意固定密度k的谓词P: k→{0,1}存在加性稀疏化。
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来源期刊
ACM Transactions on Algorithms
ACM Transactions on Algorithms COMPUTER SCIENCE, THEORY & METHODS-MATHEMATICS, APPLIED
CiteScore
3.30
自引率
0.00%
发文量
50
审稿时长
6-12 weeks
期刊介绍: ACM Transactions on Algorithms welcomes submissions of original research of the highest quality dealing with algorithms that are inherently discrete and finite, and having mathematical content in a natural way, either in the objective or in the analysis. Most welcome are new algorithms and data structures, new and improved analyses, and complexity results. Specific areas of computation covered by the journal include combinatorial searches and objects; counting; discrete optimization and approximation; randomization and quantum computation; parallel and distributed computation; algorithms for graphs, geometry, arithmetic, number theory, strings; on-line analysis; cryptography; coding; data compression; learning algorithms; methods of algorithmic analysis; discrete algorithms for application areas such as biology, economics, game theory, communication, computer systems and architecture, hardware design, scientific computing
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