Largest Chordal and Interval Subgraphs Faster than $$2^n$$ 2 n

Bliznets, Ivan; Fomin, Fedor V.; Pilipczuk, Michał; Villanger, Yngve

doi:10.1007/s00453-015-0054-2

Cited by 7 publications

(7 citation statements)

References 25 publications

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Section: Bidimensionalitymentioning

confidence: 53%

“…Similar "races" for the best exponential-time algorithm are known for many other problems [43]. Let us remark that for some problems just beating the trivial 1) running time is already highly nontrivial [10,26,66]. For the Maximum Independent Set problem on graphs of maximum degree 3, the current best algorithm has running time 1.0836 n • n O(1) [76].…”

Section: Bidimensionalitymentioning

confidence: 69%

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Four Shorts Stories on Surprising Algorithmic Uses of Treewidth

Marx

2020

Treewidth, Kernels, and Algorithms

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Section: Bidimensionalitymentioning

confidence: 53%

Section: Bidimensionalitymentioning

confidence: 69%

Four Shorts Stories on Surprising Algorithmic Uses of Treewidth

Marx

2020

Treewidth, Kernels, and Algorithms

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“…Indeed, for various classes of graphs C , algorithms with running time O((2 − ) n ) for some > 0 are known; see e.g. [5,[15][16][17]31] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Subexponential-Time Algorithms for Finding Large Induced Sparse Subgraphs

et al. 2020

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“…(We defer their definitions to the next section.) Many algorithms have been developed for vertex deletion problems to chordal graphs and its subclasses,-most notably (unit) interval graphs, cluster graphs, and split graphs; see, e.g., [17,4,10,9,8,34,12,25, 1] for a partial list. After the long progress of algorithmic achievements, some natural questions arise: What is the complexity of transforming a chordal graph to a (unit) interval graph, a cluster graph, a split graph, or a member of some other subclass of chordal graphs?…”

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confidence: 99%

“…(a) 2K 2 (b) P 4 (c) C 4 (d) claw (K 1,3 ) (e) diamond (f) net (g) tent (h) rising sun Figure 1: Small subgraphs.…”

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Vertex deletion problems on chordal graphs

Cao

Otachi

et al. 2018

Theoretical Computer Science

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Containing many classic optimization problems, the family of vertex deletion problems has an important position in algorithm and complexity study. The celebrated result of Lewis and Yannakakis gives a complete dichotomy of their complexity. It however has nothing to say about the case when the input graph is also special. This paper initiates a systematic study of vertex deletion problems from one subclass of chordal graphs to another. We give polynomial-time algorithms or proofs of NP-completeness for most of the problems. In particular, we show that the vertex deletion problem from chordal graphs to interval graphs is NP-complete. IntroductionGenerally speaking, a vertex deletion problem asks to transform an input graph to a graph in a certain class by deleting a minimum number of vertices. Many classic optimization problems belong to the family of vertex deletion problems, and their algorithms and complexity have been intensively studied. For example, the clique problem and the independent set problem are nothing but the vertex deletion problems to complete graphs and to edgeless graphs respectively. Most interesting graph properties are hereditary: If a graph satisfies this property, then so does every induced subgraph of it. For all the vertex deletion problems to hereditary graph classes, Lewis and Yannakakis [27] have settled their complexity once and for all with a dichotomy result: They are either NP-hard or trivial. Thereafter algorithmic efforts were mostly focused on the nontrivial ones, and the major approaches include approximation algorithms [28], parameterized algorithms [6], and exact algorithms [15].Chordal graphs make one of the most important graph classes. Together with many of its subclasses, it has played important roles in the development of structural graph theory. (We defer their definitions to the next section.) Many algorithms have been developed for vertex deletion problems to chordal graphs and its subclasses,-most notably (unit) interval graphs, cluster graphs, and split graphs; see, e.g., [17,4,10,9,8,34,12,25, 1] for a partial list. After the long progress of algorithmic achievements, some natural questions arise: What is the complexity of transforming a chordal graph to a (unit) interval graph, a cluster graph, a split graph, or a member of some other subclass of chordal graphs? It is quite surprising that this type of problems has not been systematically studied, save few concrete results, e.g., the polynomial-time algorithms for the clique problem, the independent set problem, and the feedback vertex set problem (the object class being forests) [21,33].The same question can be asked for other pair of source and object graph classes. The most important source classes include planar graphs [20,18,16], bipartite graphs [32], and degree-bounded graphs [19]. As one may expect, with special properties imposed on input graphs, the problems become easier, and some of them may not remain NP-hard. Unfortunately, a clear-cut answer to them seems very unlikely, since their complexity would...

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Largest Chordal and Interval Subgraphs Faster than $$2^n$$ 2 n

Abstract: We prove that in a graph with n vertices, induced chordal and interval subgraphs with the maximum number of vertices can be found in time O(2 λn ) for some λ < 1. These are the first algorithms breaking the trivial 2 n n O(1) bound of the brute-force search for these problems.

Cited by 7 publications

References 25 publications

Four Shorts Stories on Surprising Algorithmic Uses of Treewidth

Four Shorts Stories on Surprising Algorithmic Uses of Treewidth

Subexponential-Time Algorithms for Finding Large Induced Sparse Subgraphs

Vertex deletion problems on chordal graphs

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