اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدParameterized Study of the Test Cover Problem
147
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
We carry out a systematic study of a natural covering problem, used for identification across several areas, in the realm of parameterized complexity. In the {sc Test Cover} problem we are given a set $[n]={1,...,n}$ of items together with a collection, $cal T$, of distinct subsets of these items called tests. We assume that $cal T$ is a test cover, i.e., for each pair of items there is a test in $cal T$ containing exactly one of these items. The objective is to find a minimum size subcollection of $cal T$, which is still a test cover. The generic parameterized version of {sc Test Cover} is denoted by $p(k,n,|{cal T}|)$-{sc Test Cover}. Here, we are given $([n],cal{T})$ and a positive integer parameter $k$ as input and the objective is to decide whether there is a test cover of size at most $p(k,n,|{cal T}|)$. We study four parameterizations for {sc Test Cover} and obtain the following: (a) $k$-{sc Test Cover}, and $(n-k)$-{sc Test Cover} are fixed-parameter tractable (FPT). (b) $(|{cal T}|-k)$-{sc Test Cover} and $(log n+k)$-{sc Test Cover} are W[1]-hard. Thus, it is unlikely that these problems are FPT.
قيم البحث
اقرأ أيضاً
Covering problems are fundamental classical problems in optimization, computer science and complexity theory. Typically an input to these problems is a family of sets over a finite universe and the goal is to cover the elements of the universe with a
s few sets of the family as possible. The variations of covering problems include well known problems like Set Cover, Vertex Cover, Dominating Set and Facility Location to name a few. Recently there has been a lot of study on partial covering problems, a natural generalization of covering problems. Here, the goal is not to cover all the elements but to cover the specified number of elements with the minimum number of sets. In this paper we study partial covering problems in graphs in the realm of parameterized complexity. Classical (non-partial) version of all these problems have been intensively studied in planar graphs and in graphs excluding a fixed graph $H$ as a minor. However, the techniques developed for parameterized version of non-partial covering problems cannot be applied directly to their partial counterparts. The approach we use, to show that various partial covering problems are fixed parameter tractable on planar graphs, graphs of bounded local treewidth and graph excluding some graph as a minor, is quite different from previously known techniques. The main idea behind our approach is the concept of implicit branching. We find implicit branching technique to be interesting on its own and believe that it can be used for some other problems.
We study the problem of Imbalance parameterized by the twin cover of a graph. We show that Imbalance is XP parameterized by twin cover, and FPT when parameterized by the twin cover and the size of the largest clique outside the twin cover. In contras
t, we introduce a notion of succinct representations of graphs in terms of their twin cover and demonstrate that Imbalance is NP-hard in the setting of succinct representations, even for graphs that have a twin cover of size one.
After the number of vertices, Vertex Cover is the largest of the classical graph parameters and has more and more frequently been used as a separate parameter in parameterized problems, including problems that are not directly related to the Vertex C
over. Here we consider the TREEWIDTH and PATHWIDTH problems parameterized by k, the size of a minimum vertex cover of the input graph. We show that the PATHWIDTH and TREEWIDTH can be computed in O*(3^k) time. This complements recent polynomial kernel results for TREEWIDTH and PATHWIDTH parameterized by the Vertex Cover.
The problem of publishing personal data without giving up privacy is becoming increasingly important. An interesting formalization that has been recently proposed is the $k$-anonymity. This approach requires that the rows of a table are partitioned i
n clusters of size at least $k$ and that all the rows in a cluster become the same tuple, after the suppression of some entries. The natural optimization problem, where the goal is to minimize the number of suppressed entries, is known to be APX-hard even when the records values are over a binary alphabet and $k=3$, and when the records have length at most 8 and $k=4$ . In this paper we study how the complexity of the problem is influenced by different parameters. In this paper we follow this direction of research, first showing that the problem is W[1]-hard when parameterized by the size of the solution (and the value $k$). Then we exhibit a fixed parameter algorithm, when the problem is parameterized by the size of the alphabet and the number of columns. Finally, we investigate the computational (and approximation) complexity of the $k$-anonymity problem, when restricting the instance to records having length bounded by 3 and $k=3$. We show that such a restriction is APX-hard.
Several algorithms with an approximation guarantee of $O(log n)$ are known for the Set Cover problem, where $n$ is the number of elements. We study a generalization of the Set Cover problem, called the Partition Set Cover problem. Here, the elements
are partitioned into $r$ emph{color classes}, and we are required to cover at least $k_t$ elements from each color class $mathcal{C}_t$, using the minimum number of sets. We give a randomized LP-rounding algorithm that is an $O(beta + log r)$ approximation for the Partition Set Cover problem. Here $beta$ denotes the approximation guarantee for a related Set Cover instance obtained by rounding the standard LP. As a corollary, we obtain improved approximation guarantees for various set systems for which $beta$ is known to be sublogarithmic in $n$. We also extend the LP rounding algorithm to obtain $O(log r)$ approximations for similar generalizations of the Facility Location type problems. Finally, we show that many of these results are essentially tight, by showing that it is NP-hard to obtain an $o(log r)$-approximation for any of these problems.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
