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The Limits of Local Search for the Maximum Weight Independent Set Problem in d-Claw Free Graphs

Published 7 Jun 2021 in cs.DS | (2106.03555v1)

Abstract: We consider the Maximum Weight Independent Set Problem (MWIS) in dd-claw free graphs, i.e. the task of computing an independent set of maximum weight in a given dd-claw free graph G=(V,E)G=(V,E) equipped with a positive weight function $w:V\rightarrow\mathbb{R}<em>{&gt;0}$. For k1k\geq 1, the MWIS in k+1k+1-claw free graphs generalizes the weighted kk-Set Packing Problem. Given that for k3k\geq 3, this problem does not permit a polynomial time o(klogk)o(\frac{k}{\log k})-approximation unless P=NPP=NP, most previous algorithms for both weighted kk-Set Packing and the MWIS in dd-claw free graphs rely on local search. For the last twenty years, Berman's algorithm SquareImp, which yields a d2+ϵ\frac{d}{2}+\epsilon-approximation for the MWIS in dd-claw free graphs, has remained unchallenged for both problems. Recently, it was improved by Neuwohner, obtaining an approximation guarantee slightly below d2\frac{d}{2}, and inevitably raising the question of how far one can get by using local search. In this paper, we finally answer this question asymptotically in the following sense: By considering local improvements of logarithmic size, we obtain approximation ratios of d1+ϵd2\frac{d-1+\epsilon_d}{2} for the MWIS in dd-claw free graphs for d3d\geq 3 in quasi-polynomial time, where 0ϵd10\leq \epsilon_d\leq 1 and lim</em>dϵd=0\lim</em>{d\rightarrow\infty}\epsilon_d = 0. By employing the color coding technique, we can use the previous result to obtain a polynomial time k+ϵk+12\frac{k+\epsilon_{k+1}}{2}-approximation for weighted kk-Set Packing. On the other hand, we provide examples showing that no local improvement algorithm considering local improvements of size O(log(S))\mathcal{O}(\log(|\mathcal{S}|)) with respect to some power w<sup>αw<sup>\alpha of the weight function, where αR\alpha\in\mathbb{R} is chosen arbitrarily, but fixed, can yield an approximation guarantee better than k2\frac{k}{2} for the weighted kk-Set Packing Problem with k3k\geq 3.

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