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We study the $mathcal{F}$-center problem with outliers: given a metric space $(X,d)$, a general down-closed family $mathcal{F}$ of subsets of $X$, and a parameter $m$, we need to locate a subset $Sin mathcal{F}$ of centers such that the maximum distance among the closest $m$ points in $X$ to $S$ is minimized. Our main result is a dichotomy theorem. Colloquially, we prove that there is an efficient $3$-approximation for the $mathcal{F}$-center problem with outliers if and only if we can efficiently optimize a poly-bounded linear function over $mathcal{F}$ subject to a partition constraint. One concrete upshot of our result is a polynomial time $3$-approximation for the knapsack center problem with outliers for which no (true) approximation algorithm was known.
In recent years, the capacitated center problems have attracted a lot of research interest. Given a set of vertices $V$, we want to find a subset of vertices $S$, called centers, such that the maximum cluster radius is minimized. Moreover, each cente
A bipartite graph $G=(A,B,E)$ is ${cal H}$-convex, for some family of graphs ${cal H}$, if there exists a graph $Hin {cal H}$ with $V(H)=A$ such that the set of neighbours in $A$ of each $bin B$ induces a connected subgraph of $H$. Many $mathsf{NP}$-
Given a weighted graph $G=(V,E)$ with weight functions $c:Eto mathbb{R}_+$ and $pi:Vto mathbb{R}_+$, and a subset $Usubseteq V$, the normalized cut value for $U$ is defined as the sum of the weights of edges exiting $U$ divided by the weight of verti
We study the problem of robustly estimating the mean of a $d$-dimensional distribution given $N$ examples, where most coordinates of every example may be missing and $varepsilon N$ examples may be arbitrarily corrupted. Assuming each coordinate appea
In this article, we study a generalized version of the maximum independent set and minimum dominating set problems, namely, the maximum $d$-distance independent set problem and the minimum $d$-distance dominating set problem on unit disk graphs for a