Perturbation Resilient Clustering for $k$-Center and Related Problems via LP Relaxations
Chandra Chekuri, Shalmoli Gupta

TL;DR
This paper demonstrates that standard LP relaxations are integral for 2-perturbation resilient instances of k-center problems, and introduces a new model for clustering with outliers that can be exactly solved under this resilience.
Contribution
It proves integrality of LP relaxations for 2-perturbation resilient k-center and asymmetric k-center, and introduces a new perturbation resilience model for clustering with outliers.
Findings
LP relaxation is integral for 2-perturbation resilient k-center.
A new perturbation resilience model for clustering with outliers is proposed.
Existing algorithms solve clustering with outliers exactly under 2-perturbation resilience.
Abstract
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 is said to be -perturbation resilient if the optimal solution does not change when the pairwise distances are modified by a factor of 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 -center and asymmetric -center is integral for -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…
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