Book ChapterDOI
Vertex Deletion on Split Graphs: Beyond 4-Hitting Set
Pratibha Choudhary,Pallavi Jain,R. Krithika,Vibha Sahlot +3 more
- pp 161-173
TLDR
These problems are “implicit” 4-Hitting Set, and thus admit an algorithm with running time \(\mathcal{O}^\star (3.0755^k)\), a kernel with \(\math Cal O(k^3)\) vertices, and a 4-approximation algorithm, and are studied in the realm of parameterized complexity with respect to the number of vertex deletions k as parameter.Abstract:
In vertex deletion problems on graphs, the task is to find a set of minimum number of vertices whose deletion results in a graph with some specific property. The class of vertex deletion problems contains several classical optimization problems, and has been studied extensively in algorithm design. Recently, there was a study on vertex deletion problems on split graphs. One of the results shown was that transforming a split graph into a block graph and a threshold graph using minimum number of vertex deletions is NP-hard. We call the decision version of these problems as Split to Block Vertex Deletion (SBVD) and Split to Threshold Vertex Deletion (STVD), respectively. In this paper, we study these problems in the realm of parameterized complexity with respect to the number of vertex deletions k as parameter. These problems are “implicit” 4-Hitting Set, and thus admit an algorithm with running time \(\mathcal{O}^\star (3.0755^k)\), a kernel with \(\mathcal{O}(k^3)\) vertices, and a 4-approximation algorithm. In this paper, we exploit the structure of the input graph to obtain a kernel for SBVD with \(\mathcal{O}(k^2)\) vertices and FPT algorithms for SBVD and STVD with running times \({\mathcal O^\star }(2.3028^k)\) and \({\mathcal O^\star }(2.7913^k)\).read more
Citations
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Posted Content
Faster branching algorithm for split to block vertex deletion
TL;DR: An algorithm for SBVD whose running time is O(2.203) is given and it is shown that there is a set S of at most k vertices such that G− S is a block graph.
Journal ArticleDOI
Algorithms for deletion problems on split graphs
TL;DR: In this article, the authors give algorithms with running times of O ⁎ (2.076 k ) and O⁎( 1.619 k ) for the split to block vertex deletion and split to threshold vertex deletion problems.
Posted Content
A note on the Split to Block Vertex Deletion problem.
TL;DR: An algorithm for SBVD whose running time is O^*(2.076^k) is given and it is shown that there is a set of vertices such that G-S is a block graph.
Posted Content
Algorithms for deletion problems on split graphs.
TL;DR: Algorithms for the Split to Block Vertex Deletion and Split to Threshold Vertex deletion problems, whose running times are $O^*(2.076^k)$ and $O-S$ ($G-S), respectively are given.
References
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Book
Graph Theory
TL;DR: Gaph Teory Fourth Edition is standard textbook of modern graph theory which covers the core material of the subject with concise yet reliably complete proofs, while offering glimpses of more advanced methods in each chapter by one or two deeper results.
Journal ArticleDOI
The Node-Deletion Problem for Hereditary Properties Is NP-Complete
John Lewis,Mihalis Yannakakis +1 more
TL;DR: It is shown that if Π is nontrivial and hereditary on induced subgraphs, then the node-deletion problem forΠ is NP-complete for both undirected and directed graphs.
Journal ArticleDOI
Distance-hereditary graphs
TL;DR: Distance-hereditary graphs (sensu Howorka) as mentioned in this paper are connected graphs in which all induced paths are isometric, and can be characterized in terms of the distance function d or via forbidden isometric subgraphs.
Journal ArticleDOI
Faster Parameterized Algorithms for Deletion to Split Graphs
Esha Ghosh,Sudeshna Kolay,Mrinal Kumar,Pranabendu Misra,Fahad Panolan,Ashutosh Rai,M. S. Ramanujan +6 more
TL;DR: A systematic study of parameterized complexity of the problem of deleting the minimum number of vertices or edges from a given input graph so that the resulting graph is split and an efficient fixed-parameter algorithms and polynomial sized kernels are given.
Book ChapterDOI
Iterative Compression and Exact Algorithms
TL;DR: Iterative compression has recently led to a number of breakthroughs in parameterized complexity and it is shown that the technique can also be useful in the design of exact exponential time algorithms to solve NP-hard problems.