Distributed Distance-Bounded Network Design Through Distributed Convex Programming

Authors Michael Dinitz, Yasamin Nazari

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Michael Dinitz
Yasamin Nazari

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Michael Dinitz and Yasamin Nazari. Distributed Distance-Bounded Network Design Through Distributed Convex Programming. In 21st International Conference on Principles of Distributed Systems (OPODIS 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 95, pp. 5:1-5:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


Solving linear programs is often a challenging task in distributed settings. While there are good algorithms for solving packing and covering linear programs in a distributed manner (Kuhn et al. 2006), this is essentially the only class of linear programs for which such an algorithm is known. In this work we provide a distributed algorithm for solving a different class of convex programs which we call “distance-bounded network design convex programs”. These can be thought of as relaxations of network design problems in which the connectivity requirement includes a distance constraint (most notably, graph spanners). Our algorithm runs in O((D/ε) log n) rounds in the LOCAL model and with high probability finds a (1+ε)-approximation to the optimal LP solution for any 0 < ε ≤ 1, where D is the largest distance constraint. While solving linear programs in a distributed setting is interesting in its own right, this class of convex programs is particularly important because solving them is often a crucial step when designing approximation algorithms. Hence we almost immediately obtain new and improved distributed approximation algorithms for a variety of network design problems, including Basic 3- and 4-Spanner, Directed k-Spanner, Lowest Degree k-Spanner, and Shallow-Light Steiner Network Design with a spanning demand graph. Our algorithms do not require any “heavy” computation and essentially match the best-known centralized approximation algorithms, while previous approaches which do not use heavy computation give approximations which are worse than the best-known centralized bounds.
  • distributed algorithms
  • approximation algorithms
  • convex programming


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