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Book ChapterDOI

Hitting and Covering Partially

TL;DR: This paper designs a kernel for Partial \(d\)-Exact SC in which sizes of the universe and the family are bounded by \({\mathcal {O}}(k^{d+1})\), and studies variants of d-Hitting Set and d-Set Cover, which are called Partial d -Hitting set (Partial \(d)-HS) and Partial \( d-Exact Set Cover (Partic \(d-Exacts SC), respectively.
Abstract: d-Hitting Set and d-Set Cover are among the classical NP-hard problems. In this paper, we study variants of d-Hitting Set and d-Set Cover, which are called Partial d -Hitting Set (Partial \(d\)-HS) and Partial \(d\)-Exact Set Cover (Partial \(d\)-Exact SC), respectively. In Partial \(d\)-HS, given a universe U, a family \({\mathcal F}\), of sets of size at most d over U, and integers k and t, the objective is to decide if there exists a \(S \subseteq U\) of size at most k such that S intersects with at least t sets in \(\mathcal {F}\). We obtain a kernel for Partial \(d\)-HS in which the size of the universe is bounded by \({\mathcal {O}}(dt)\) and the size of the family is bounded by \({\mathcal {O}}(dt^2)\). Using this result, we obtain a kernel for Partial Vertex Cover (PVC) with \({\mathcal {O}}(t)\) vertices, where t is the number of edges to be covered. Next, we study the Partial \(d\)-Exact SC problem, where, given a universe U, a family \({\mathcal F}\), of sets of size exactly d over U, and integers k and t, the objective is to decide if there is \({\mathcal S}\subseteq {\mathcal F}\) of size at most k, such that \({\mathcal S}\) covers at least t elements in U. We design a kernel for Partial \(d\)-Exact SC in which sizes of the universe and the family are bounded by \({\mathcal {O}}(k^{d+1})\). Finally, we study a special case of Partial \(d\)-HS, when \(d=2\), and design an exact exponential time algorithm with running time \({\mathcal {O}}(1.731^nn^{{\mathcal {O}}(1)})\).
Citations
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Proceedings Article
09 Jul 2018
TL;DR: This work revisits the parameterized complexity of WINNER DETERMINATION for these rules by considering several important single parameters, combined parameters, and structural parameters, aiming at detecting as many fixed-parameter tractability results as possible.
Abstract: We study the k -committee selection rules minimax approval, proportional approval, and Chamberlin-Courant's approval. It is known that WINNER DETERMINATION for these rules is NP-hard. Moreover, the parameterized complexity of the problem has also been studied with respect to some natural parameters. However, there are still numerous parameterizations that have not been considered. We revisit the parameterized complexity of WINNER DETERMINATION for these rules by considering several important single parameters, combined parameters, and structural parameters, aiming at detecting as many fixed-parameter tractability results as possible.

11 citations


Cites background or methods from "Hitting and Covering Partially"

  • ...However, we show that WD-MAV and WDCCAV are W[1]-hard with respect to the combined parameter k̄+4V....

    [...]

  • ...we show that WD-MAV, WD-CCAV, and WD-PAV are W[1]-hard....

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  • ...With respect to the combined parameter k+4V, we develop an FPT-algorithm for WD-MAV but show that WD-CCAV is W[1]-hard....

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  • ...Corollary 1 WD-MAV is W[1]-hard with respect to k̄ even when every voter approves exactly two candidates....

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  • ...Regarding MAV, the NP-hardness proof by LeGrand [41] actually already implied that WD-MAV is W[1]-hard with respect to k̄....

    [...]

Journal ArticleDOI
TL;DR: In this article , the authors studied the parameterized complexity of winner determination problems for three prevalent k-committee selection rules, namely the minimax approval voting (MAV), the proportional approval voting and the Chamberlin-Courant's approval voting.
Abstract: Abstract We study the parameterized complexity of winner determination problems for three prevalent k -committee selection rules, namely the minimax approval voting (MAV), the proportional approval voting (PAV), and the Chamberlin–Courant’s approval voting (CCAV). It is known that these problems are computationally hard. Although they have been studied from the parameterized complexity point of view with respect to several natural parameters, many of them turned out to be -hard or -hard. Aiming at obtaining plentiful fixed-parameter algorithms, we revisit these problems by considering more natural single parameters, combined parameters, and structural parameters.
Posted Content
TL;DR: In this article, the authors study winner determination for three prevalent $k$-committee selection rules, namely, minimax approval voting, proportional approval voting and Chamberlin-Courant's approval voting.
Abstract: We study winner determination for three prevalent $k$-committee selection rules, namely minimax approval voting (MAV), proportional approval voting (PAV), and Chamberlin-Courant's approval voting (CCAV). It is known that winner determination for these rules is NP-hard. Parameterized complexity of the problem has been studied with respect to some natural parameters recently. However, there are still numerous parameters that have not been considered. We revisit the parameterized complexity of winner determination by considering several important single parameters, combined parameters, and structural parameters, aiming at detecting more interesting parameters leading to fixed-parameter tractability res
References
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Book ChapterDOI
TL;DR: The work of Dantzig, Fulkerson, Hoffman, Edmonds, Lawler and other pioneers on network flows, matching and matroids acquainted me with the elegant and efficient algorithms that were sometimes possible.
Abstract: Throughout the 1960s I worked on combinatorial optimization problems including logic circuit design with Paul Roth and assembly line balancing and the traveling salesman problem with Mike Held. These experiences made me aware that seemingly simple discrete optimization problems could hold the seeds of combinatorial explosions. The work of Dantzig, Fulkerson, Hoffman, Edmonds, Lawler and other pioneers on network flows, matching and matroids acquainted me with the elegant and efficient algorithms that were sometimes possible. Jack Edmonds’ papers and a few key discussions with him drew my attention to the crucial distinction between polynomial-time and superpolynomial-time solvability. I was also influenced by Jack’s emphasis on min-max theorems as a tool for fast verification of optimal solutions, which foreshadowed Steve Cook’s definition of the complexity class NP. Another influence was George Dantzig’s suggestion that integer programming could serve as a universal format for combinatorial optimization problems.

8,644 citations

01 Jan 1972
TL;DR: Throughout the 1960s I worked on combinatorial optimization problems including logic circuit design with Paul Roth and assembly line balancing and the traveling salesman problem with Mike Held, which made me aware of the importance of distinction between polynomial-time and superpolynomial-time solvability.
Abstract: Throughout the 1960s I worked on combinatorial optimization problems including logic circuit design with Paul Roth and assembly line balancing and the traveling salesman problem with Mike Held. These experiences made me aware that seemingly simple discrete optimization problems could hold the seeds of combinatorial explosions. The work of Dantzig, Fulkerson, Hoffman, Edmonds, Lawler and other pioneers on network flows, matching and matroids acquainted me with the elegant and efficient algorithms that were sometimes possible. Jack Edmonds’ papers and a few key discussions with him drew my attention to the crucial distinction between polynomial-time and superpolynomial-time solvability. I was also influenced by Jack’s emphasis on min-max theorems as a tool for fast verification of optimal solutions, which foreshadowed Steve Cook’s definition of the complexity class NP. Another influence was George Dantzig’s suggestion that integer programming could serve as a universal format for combinatorial optimization problems.

7,714 citations

Book
02 Jul 2001
TL;DR: Covering the basic techniques used in the latest research work, the author consolidates progress made so far, including some very recent and promising results, and conveys the beauty and excitement of work in the field.
Abstract: Covering the basic techniques used in the latest research work, the author consolidates progress made so far, including some very recent and promising results, and conveys the beauty and excitement of work in the field. He gives clear, lucid explanations of key results and ideas, with intuitive proofs, and provides critical examples and numerous illustrations to help elucidate the algorithms. Many of the results presented have been simplified and new insights provided. Of interest to theoretical computer scientists, operations researchers, and discrete mathematicians.

4,290 citations

Book
16 Aug 2021

2,526 citations

Book
01 Jan 2006
TL;DR: This paper discusses Fixed-Parameter Algorithms, Parameterized Complexity Theory, and Selected Case Studies, and some of the techniques used in this work.
Abstract: PART I: FOUNDATIONS 1. Introduction to Fixed-Parameter Algorithms 2. Preliminaries and Agreements 3. Parameterized Complexity Theory - A Primer 4. Vertex Cover - An Illustrative Example 5. The Art of Problem Parameterization 6. Summary and Concluding Remarks PART II: ALGORITHMIC METHODS 7. Data Reduction and Problem Kernels 8. Depth-Bounded Search Trees 9. Dynamic Programming 10. Tree Decompositions of Graphs 11. Further Advanced Techniques 12. Summary and Concluding Remarks PART III: SOME THEORY, SOME CASE STUDIES 13. Parameterized Complexity Theory 14. Connections to Approximation Algorithms 15. Selected Case Studies 16. Zukunftsmusik References Index

1,730 citations