Tight Analysis of a MultipleSwap Heuristic for Budgeted RedBlue Median
Abstract
Budgeted RedBlue Median is a generalization of classic $k$Median in that there are two sets of facilities, say $\mathcal{R}$ and $\mathcal{B}$, that can be used to serve clients located in some metric space. The goal is to open $k_r$ facilities in $\mathcal{R}$ and $k_b$ facilities in $\mathcal{B}$ for some given bounds $k_r, k_b$ and connect each client to their nearest open facility in a way that minimizes the total connection cost. We extend work by Hajiaghayi, Khandekar, and Kortsarz [2012] and show that a multipleswap local search heuristic can be used to obtain a $(5+\epsilon)$approximation for Budgeted RedBlue Median for any constant $\epsilon > 0$. This is an improvement over their single swap analysis and beats the previous best approximation guarantee of 8 by Swamy [2014]. We also present a matching lower bound showing that for every $p \geq 1$, there are instances of Budgeted RedBlue Median with local optimum solutions for the $p$swap heuristic whose cost is $5 + \Omega\left(\frac{1}{p}\right)$ times the optimum solution cost. Thus, our analysis is tight up to the lower order terms. In particular, for any $\epsilon > 0$ we show the singleswap heuristic admits local optima whose cost can be as bad as $7\epsilon$ times the optimum solution cost.
 Publication:

arXiv eprints
 Pub Date:
 March 2016
 arXiv:
 arXiv:1603.00973
 Bibcode:
 2016arXiv160300973F
 Keywords:

 Computer Science  Data Structures and Algorithms