Parameterized Complexity of d-Hitting Set with Quotas
25 Jan 2021-pp 293-307
TL;DR: In this paper, the authors studied a variant of the classic d-Hitting Set problem with lower and upper capacity constraints, where each set in the family is of size at most d, a non-negative integer k; and additionally two functions are defined.
Abstract: In this paper we study a variant of the classic d -Hitting Set problem with lower and upper capacity constraints, say A and B, respectively. The input to the problem consists of a universe U, a set family, \(\mathscr {S} \), of sets over U, where each set in the family is of size at most d, a non-negative integer k; and additionally two functions \(\alpha :\mathscr {S} \rightarrow \{1,\ldots ,A\}\) and \(\beta :\mathscr {S} \rightarrow \{1,\ldots ,B\}\). The goal is to decide if there exists a hitting set of size at most k such that for every set S in the family \(\mathscr {S} \), the solution contains at least \(\alpha (S)\) elements and at most \(\beta (S)\) elements from S. We call this the \((A, B)\)-Multi d-Hitting Set problem. We study the problem in the realm of parameterized complexity. We show that \((A, B)\)-Multi d-Hitting Set can be solved in \(\mathcal {O}^{\star }(d^{k}) \) time. For the special case when \(d=3\) and \(d=4\), we have an improved bound of \(\mathcal {O}^\star (2.2738^k)\) and \(\mathcal {O}^\star (3.562^{k})\), respectively. The former matches the running time of the classical 3-Hitting Set problem. Furthermore, we show that if we do not have an upper bound constraint and the lower bound constraint is same for all the sets in the family, say \(A>1\), then the problem can be solved even faster than d-Hitting Set.
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Book•
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TL;DR: A kernelization algorithm for the 3-Hitting Set problem is presented along with a general kernelization for d-H hitting Set, which guarantees a kernel whose order does not exceed (2d-1)k^d^-^1+k.
Abstract: For a given parameterized problem, @p, a kernelization algorithm is a polynomial-time pre-processing procedure that transforms an arbitrary instance of @p into an equivalent one whose size depends only on the input parameter(s). The resulting instance is called a problem kernel. In this paper, a kernelization algorithm for the 3-Hitting Set problem is presented along with a general kernelization for d-Hitting Set. For 3-Hitting Set, an arbitrary instance is reduced into an equivalent one that contains at most 5k^2+k elements. This kernelization is an improvement over previously known methods that guarantee cubic-order kernels. Our method is used also to obtain quadratic kernels for several other problems. For a constant d>=3, a kernelization of d-Hitting Set is achieved by a non-trivial generalization of the 3-Hitting Set method, and guarantees a kernel whose order does not exceed (2d-1)k^d^-^1+k.
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