Oct 20, 2017 · Abstract:Kernelization is an important tool in parameterized algorithmics. Given an input instance accompanied by a parameter, ...
In this paper we initiate a systematic approach to derive kernelization lower bounds for problems in P. We demonstrate our techniques at the example of subgraph ...
We adapt the diminisher framework [9, 14] to prove kernelization lower bounds for problems in P. Our results concern the H-Subgraph Isomorphism (H-SI) problem1.
Jul 3, 2018 · In this paper, we provide a first conceptual study on limits of kernelization for several polynomial-time solvable problems.
Kernelization Lower Bounds for Finding Constant-Size Subgraphs
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In this paper, we provide a first conceptual study on limits of kernelization for several polynomial-time solvable problems. For instance, we consider the ...
In this paper, we provide a first conceptual study on limits of kernelization for several polynomial-time solvable problems. For instance, we consider the ...
This paper provides a first conceptual study on limits of kernelization for several polynomial-time solvable problems, and proves that a linear-time ...
Kernel-Size Lower Bounds. Page 40. One approach to kernelization: sparsification. Reasonable idea, but no nontrivial kernel size bounds known for OR=(L), AND=(L) ...
The problem is said to admit a kernel if, in polynomial time, we can reduce the size of the instance x to a function in k , while preserving the answer. The ...
Abstract. We introduce a new technique for proving kernelization lower bounds, called cross-composition. A classical problem L cross-composes into a ...