87 research outputs found

    Solving MaxSAT and #SAT on structured CNF formulas

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    In this paper we propose a structural parameter of CNF formulas and use it to identify instances of weighted MaxSAT and #SAT that can be solved in polynomial time. Given a CNF formula we say that a set of clauses is precisely satisfiable if there is some complete assignment satisfying these clauses only. Let the ps-value of the formula be the number of precisely satisfiable sets of clauses. Applying the notion of branch decompositions to CNF formulas and using ps-value as cut function, we define the ps-width of a formula. For a formula given with a decomposition of polynomial ps-width we show dynamic programming algorithms solving weighted MaxSAT and #SAT in polynomial time. Combining with results of 'Belmonte and Vatshelle, Graph classes with structured neighborhoods and algorithmic applications, Theor. Comput. Sci. 511: 54-65 (2013)' we get polynomial-time algorithms solving weighted MaxSAT and #SAT for some classes of structured CNF formulas. For example, we get O(m2(m+n)s)O(m^2(m + n)s) algorithms for formulas FF of mm clauses and nn variables and size ss, if FF has a linear ordering of the variables and clauses such that for any variable xx occurring in clause CC, if xx appears before CC then any variable between them also occurs in CC, and if CC appears before xx then xx occurs also in any clause between them. Note that the class of incidence graphs of such formulas do not have bounded clique-width

    Obstruction characterization of co-TT graphs

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    Threshold tolerance graphs and their complement graphs ( known as co-TT graphs) were introduced by Monma, Reed and Trotter[24]. Introducing the concept of negative interval Hell et al.[19] defined signed-interval bigraphs/digraphs and have shown that they are equivalent to several seemingly different classes of bigraphs/digraphs. They have also shown that co-TT graphs are equivalent to symmetric signed-interval digraphs. In this paper we characterize signed-interval bigraphs and signed-interval graphs respectively in terms of their biadjacency matrices and adjacency matrices. Finally, based on the geometric representation of signed-interval graphs we have setteled the open problem of forbidden induced subgraph characterization of co-TT graphs posed by Monma, Reed and Trotter in the same paper.Comment: arXiv admin note: substantial text overlap with arXiv:2206.0591

    Graph classes equivalent to 12-representable graphs

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    Jones et al. (2015) introduced the notion of uu-representable graphs, where uu is a word over {1,2}\{1, 2\} different from 22⋯222\cdots2, as a generalization of word-representable graphs. Kitaev (2016) showed that if uu is of length at least 3, then every graph is uu-representable. This indicates that there are only two nontrivial classes in the theory of uu-representable graphs: 11-representable graphs, which correspond to word-representable graphs, and 12-representable graphs. This study deals with 12-representable graphs. Jones et al. (2015) provided a characterization of 12-representable trees in terms of forbidden induced subgraphs. Chen and Kitaev (2022) presented a forbidden induced subgraph characterization of a subclass of 12-representable grid graphs. This paper shows that a bipartite graph is 12-representable if and only if it is an interval containment bigraph. The equivalence gives us a forbidden induced subgraph characterization of 12-representable bipartite graphs since the list of minimal forbidden induced subgraphs is known for interval containment bigraphs. We then have a forbidden induced subgraph characterization for grid graphs, which solves an open problem of Chen and Kitaev (2022). The study also shows that a graph is 12-representable if and only if it is the complement of a simple-triangle graph. This equivalence indicates that a necessary condition for 12-representability presented by Jones et al. (2015) is also sufficient. Finally, we show from these equivalences that 12-representability can be determined in O(n2)O(n^2) time for bipartite graphs and in O(n(mˉ+n))O(n(\bar{m}+n)) time for arbitrary graphs, where nn and mˉ\bar{m} are the number of vertices and edges of the complement of the given graph.Comment: 12 pages, 6 figure
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