English

Approximation Algorithm for Sparsest k-Partitioning

Data Structures and Algorithms 2013-10-09 v2

Abstract

Given a graph GG, the sparsest-cut problem asks to find the set of vertices SS which has the least expansion defined as ϕG(S):=w(E(S,Sˉ))min{w(S),w(Sˉ)},\phi_G(S) := \frac{w(E(S,\bar{S}))}{\min \set{w(S), w(\bar{S})}}, where ww is the total edge weight of a subset. Here we study the natural generalization of this problem: given an integer kk, compute a kk-partition {P1,,Pk}\set{P_1, \ldots, P_k} of the vertex set so as to minimize ϕk({P1,,Pk}):=maxiϕG(Pi). \phi_k(\set{P_1, \ldots, P_k}) := \max_i \phi_G(P_i). Our main result is a polynomial time bi-criteria approximation algorithm which outputs a (1\e)k(1 - \e)k-partition of the vertex set such that each piece has expansion at most Oε(lognlogk)O_{\varepsilon}(\sqrt{\log n \log k}) times OPTOPT. We also study balanced versions of this problem.

Keywords

Cite

@article{arxiv.1306.4384,
  title  = {Approximation Algorithm for Sparsest k-Partitioning},
  author = {Anand Louis and Konstantin Makarychev},
  journal= {arXiv preprint arXiv:1306.4384},
  year   = {2013}
}
R2 v1 2026-06-22T00:36:27.844Z