Mathias Hauptmann

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We study the approximation complexity of the Metric Dimension problem in bounded degree, dense as well as in general graphs. For the general case, we prove that the Metric Dimension problem is not approximable within (1 − ǫ) lnn for any ǫ > 0, unless NP ⊆ DTIME(nlog logn), and we give an approximation algorithm which matches the lower bound. Even for(More)
We give logarithmic lower bounds for the approximability of the Minimum Dominating Set problem in connected (α, β)-Power Law Graphs. We give also a best up to now upper approximation bound on the problem for the case of the parameters β > 2. We develop also a new functional method for proving lower approximation bounds and display a sharp phase transition(More)
We give the first nonconstant lower bounds for the approximability of the Independent Set Problem on the Power Law Graphs. These bounds are of the form n in the case when the power law exponent satisfies β < 1. In the case when β = 1, the lower bound is of the form log(n) . The embedding technique used in the proof could also be of independent interest.
In this paper we construct an approximation algorithm for the Minimum Vertex Cover Problem (Min-VC) with an expected approximation ratio of 2 − ζ(β)−1− 1 2β 2βζ(β−1)ζ(β) for random Power Law Graphs (PLG) in the (α, β)-model of Aiello et. al.. We obtain this result by combining the Nemhauser and Trotter approach for Min-VC with a new deterministic rounding(More)
In this paper we study the MAX-CUT problem on power law graphs (PLGs) with power law exponent β. We prove some new approximability results on that problem. In particular we show that there exist polynomial time approximation schemes (PTAS) for MAX-CUT on PLGs for the power law exponent β in the interval (0, 2). For β > 2 we show that for some ε > 0, MAX-CUT(More)