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Kernelization lower bounds for finding constant-size subgraphs.

Fluschnik, T. and Mertzios, G.B. and Nichterlein, A. (2018) 'Kernelization lower bounds for finding constant-size subgraphs.', in Sailing routes in the world of computation : 14th Conference on Computability in Europe, CiE 2018, Kiel, Germany, July 30-August 3, 2018. Proceedings. Cham: Springer, pp. 183-193. Lecture notes in computer science. (10936).


Kernelization is an important tool in parameterized algorithmics. Given an input instance accompanied by a parameter, the goal is to compute in polynomial time an equivalent instance of the same problem such that the size of the reduced instance only depends on the parameter and not on the size of the original instance. In this paper, we provide a first conceptual study on limits of kernelization for several polynomial-time solvable problems. For instance, we consider the problem of finding a triangle with negative sum of edge weights parameterized by the maximum degree of the input graph. We prove that a linear-time computable strict kernel of truly subcubic size for this problem violates the popular APSP-conjecture.

Item Type:Book chapter
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Publisher statement:This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science. The final authenticated version is available online at:
Date accepted:06 April 2018
Date deposited:13 September 2018
Date of first online publication:03 July 2018
Date first made open access:No date available

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