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We investigate the following above-guarantee parameterization of the classical Vertex Cover problem: Given a graph $G$ and $kinmathbb{N}$ as input, does $G$ have a vertex cover of size at most $(2LP-MM)+k$? Here $MM$ is the size of a maximum matching of $G$, $LP$ is the value of an optimum solution to the relaxed (standard) LP for Vertex Cover on $G$, and $k$ is the parameter. Since $(2LP-MM)geq{LP}geq{MM}$, this is a stricter parameterization than those---namely, above-$MM$, and above-$LP$---which have been studied so far. We prove that Vertex Cover is fixed-parameter tractable for this stricter parameter $k$: We derive an algorithm which solves Vertex Cover in time $O^{*}(3^{k})$, pushing the envelope further on the parameterized tractability of Vertex Cover.
We consider a CNF formula $F$ as a multiset of clauses: $F={c_1,..., c_m}$. The set of variables of $F$ will be denoted by $V(F)$. Let $B_F$ denote the bipartite graph with partite sets $V(F)$ and $F$ and with an edge between $v in V(F)$ and $c in F$
When modeling an application of practical relevance as an instance of a combinatorial problem X, we are often interested not merely in finding one optimal solution for that instance, but in finding a sufficiently diverse collection of good solutions.
Reconfiguration schedules, i.e., sequences that gradually transform one solution of a problem to another while always maintaining feasibility, have been extensively studied. Most research has dealt with the decision problem of whether a reconfigurati
We present a massively parallel algorithm, with near-linear memory per machine, that computes a $(2+varepsilon)$-approximation of minimum-weight vertex cover in $O(loglog d)$ rounds, where $d$ is the average degree of the input graph. Our result fi
A common approach for designing scalable algorithms for massive data sets is to distribute the computation across, say $k$, machines and process the data using limited communication between them. A particularly appealing framework here is the simulta