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          <dc:title>A 4/3-Approximation Algorithm for the Minimum 2-Edge Connected Multisubgraph Problem in the Half-Integral Case</dc:title>
          <dc:creator>Boyd, Sylvia</dc:creator>
          <dc:creator>Cheriyan, Joseph</dc:creator>
          <dc:creator>Cummings, Robert</dc:creator>
          <dc:creator>Grout, Logan</dc:creator>
          <dc:creator>Ibrahimpur, Sharat</dc:creator>
          <dc:creator>Szigeti, Zoltán</dc:creator>
          <dc:creator>Wang, Lu</dc:creator>
          <dc:subject>2-Edge Connectivity</dc:subject>
          <dc:subject>Approximation Algorithms</dc:subject>
          <dc:subject>Subtour LP for TSP</dc:subject>
          <dc:description>Given a connected undirected graph G ̅ on n vertices, and non-negative edge costs c, the 2ECM problem is that of finding a 2-edge connected spanning multisubgraph of G ̅ of minimum cost. The natural linear program (LP) for 2ECM, which coincides with the subtour LP for the Traveling Salesman Problem on the metric closure of G ̅, gives a lower bound on the optimal cost. For instances where this LP is optimized by a half-integral solution x, Carr and Ravi (1998) showed that the integrality gap is at most 4/3: they show that the vector 4/3 x dominates a convex combination of incidence vectors of 2-edge connected spanning multisubgraphs of G ̅.&#13;
We present a simpler proof of the result due to Carr and Ravi by applying an extension of Lovász’s splitting-off theorem. Our proof naturally leads to a 4/3-approximation algorithm for half-integral instances. Given a half-integral solution x to the LP for 2ECM, we give an O(n²)-time algorithm to obtain a 2-edge connected spanning multisubgraph of G ̅ whose cost is at most 4/3 c^T x.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Sylvia Boyd and Joseph Cheriyan and Robert Cummings and Logan Grout and Sharat Ibrahimpur and Zoltán Szigeti and Lu Wang</dc:contributor>
          <dc:date>2020</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 176, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2020)</dc:relation>
          <dc:type>InProceedings</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.APPROX/RANDOM.2020.61</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-126643</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2020.61</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/3.0/legalcode</dc:rights>
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