New algorithms for maximum disjoint paths based on tree-likeness

DOI10.1007/S10107-017-1199-3zbMATH Open1401.68361DBLPjournals/mp/FleszarMS18arXiv1603.01740OpenAlexW2885925565WikidataQ59602738 ScholiaQ59602738MaRDI QIDQ1785205

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Publication date: 28 September 2018

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Abstract: We study the classical NP-hard problems of finding maximum-size subsets from given sets of $k$ terminal pairs that can be routed via edge-disjoint paths (MaxEDP) or node-disjoint paths (MaxNDP) in a given graph. The approximability of MaxEDP/NDP is currently not well understood; the best known lower bound is $Omega(lo{g}^{1/2-epsilon}n)$, assuming NP$otsubseteq$ZPTIME$(n^{mathrmpolylogn})$. This constitutes a significant gap to the best known approximation upper bound of $O(sqrtn)$ due to Chekuri et al. (2006) and closing this gap is currently one of the big open problems in approximation algorithms. In their seminal paper, Raghavan and Thompson (Combinatorica, 1987) introduce the technique of randomized rounding for LPs; their technique gives an $O(1)$-approximation when edges (or nodes) may be used by $O(fraclognloglogn)$ paths. In this paper, we strengthen the above fundamental results. We provide new bounds formulated in terms of the feedback vertex set number $r$ of a graph, which measures its vertex deletion distance to a forest. In particular, we obtain the following. * For MaxEDP, we give an $O(sqrtrcdotlo{g}^{1.5}kr)$-approximation algorithm. As $rleqn$, up to logarithmic factors, our result strengthens the best known ratio $O(sqrtn)$ due to Chekuri et al. * Further, we show how to route $Omega(mathrmOPT)$ pairs with congestion $O(fraclogkrloglogkr)$, strengthening the bound obtained by the classic approach of Raghavan and Thompson. * For MaxNDP, we give an algorithm that gives the optimal answer in time $(k+r{)}^{O(r)}cdotn$. If $r$ is at most triple-exponential in $k$, this improves the best known algorithm for MaxNDP with parameter $k$, by Kawarabayashi and Wollan (STOC 2010). We complement these positive results by proving that MaxEDP is NP-hard even for $r=1$, and MaxNDP is W$[1]$-hard for parameter $r$.

Full work available at URL: https://arxiv.org/abs/1603.01740

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