Perturbation Resilient Clustering for $k$-Center and Related Problems via LP Relaxations

We consider clustering in the perturbation resilience model that has been studied since the work of Bilu and Linial [ICS, 2010] and Awasthi, Blum and Sheffet [Inf. Proc. Lett., 2012]. A clustering instance $I$ is said to be $\alpha$-perturbation resilient if the optimal solution does not change when the pairwise distances are modified by a factor of $\alpha$ and the perturbed distances satisfy the metric property --- this is the metric perturbation resilience property introduced in Angelidakis et. al. [STOC, 2010] and a weaker requirement than prior models. We make two high-level contributions. 1) We show that the natural LP relaxation of $k$-center and asymmetric $k$-center is integral for $2$-perturbation resilient instances. We belive that demonstrating the goodness of standard LP relaxations complements existing results that are based on combinatorial algorithms designed for the perturbation model. 2) We define a simple new model of perturbation resilience for clustering with \emph{outliers}. Using this model we show that the unified MST and dynamic programming based algorithm proposed by Angelidakis et. al. [STOC, 2010] exactly solves the clustering with outliers problem for several common center based objectives (like $k$-center, $k$-means, $k$-median) when the instances is $2$-perturbation resilient. We further show that a natural LP relxation is integral for $2$-perturbation resilient instances of \kcenter with outliers.