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        <identifier>oai:drops-oai.dagstuhl.de:16175</identifier>
        <datestamp>2024-03-06T10:57:04Z</datestamp>
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          <dc:title>Stable Approximation Algorithms for the Dynamic Broadcast Range-Assignment Problem</dc:title>
          <dc:creator>de Berg, Mark</dc:creator>
          <dc:creator>Sadhukhan, Arpan</dc:creator>
          <dc:creator>Spieksma, Frits</dc:creator>
          <dc:subject>Computational geometry</dc:subject>
          <dc:subject>online algorithms</dc:subject>
          <dc:subject>broadcast range assignment</dc:subject>
          <dc:subject>stable approximation schemes</dc:subject>
          <dc:description>Let P be a set of points in ℝ^d (or some other metric space), where each point p ∈ P has an associated transmission range, denoted ρ(p). The range assignment ρ induces a directed communication graph G_{ρ}(P) on P, which contains an edge (p,q) iff |pq| ⩽ ρ(p). In the broadcast range-assignment problem, the goal is to assign the ranges such that G_{ρ}(P) contains an arborescence rooted at a designated root node and the cost ∑_{p ∈ P} ρ(p)² of the assignment is minimized.&#13;
We study the dynamic version of this problem. In particular, we study trade-offs between the stability of the solution - the number of ranges that are modified when a point is inserted into or deleted from P - and its approximation ratio. To this end we introduce the concept of k-stable algorithms, which are algorithms that modify the range of at most k points when they update the solution. We also introduce the concept of a stable approximation scheme, or SAS for short. A SAS is an update algorithm alg that, for any given fixed parameter ε &gt; 0, is k(ε)-stable and that maintains a solution with approximation ratio 1+ε, where the stability parameter k(ε) only depends on ε and not on the size of P. We study such trade-offs in three settings.  &#13;
- For the problem in ℝ¹, we present a SAS with k(ε) = O(1/ε). Furthermore, we prove that this is tight in the worst case: any SAS for the problem must have k(ε) = Ω(1/ε). We also present algorithms with very small stability parameters: a 1-stable (6+2√5)-approximation algorithm - this algorithm can only handle insertions - a (trivial) 2-stable 2-approximation algorithm, and a 3-stable 1.97-approximation algorithm. &#13;
- For the problem in 𝕊¹ (that is, when the underlying space is a circle) we prove that no SAS exists. This is in spite of the fact that, for the static problem in 𝕊¹, we prove that an optimal solution can always be obtained by cutting the circle at an appropriate point and solving the resulting problem in ℝ¹. &#13;
- For the problem in ℝ², we also prove that no SAS exists, and we present a O(1)-stable O(1)-approximation algorithm.  Most results generalize to when the range-assignment cost is ∑_{p ∈ P} ρ(p)^{α}, for some constant α &gt; 1. All omitted theorems and proofs are available in the full version of the paper [Mark de Berg et al., 2021].</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Mark de Berg and Arpan Sadhukhan and Frits Spieksma</dc:contributor>
          <dc:date>2022</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 227, 18th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2022)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
          <dc:type>doc-type:ResearchArticle</dc:type>
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          <dc:format>application/pdf</dc:format>
          <dc:identifier>doi:10.4230/LIPIcs.SWAT.2022.15</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-161756</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SWAT.2022.15</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
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