Distributed Distance-Bounded Network Design Through Distributed Convex Programming

TL;DR

This paper introduces a distributed algorithm for solving a new class of convex programs called distance-bounded network design programs, enabling improved distributed approximation algorithms for various network design problems.

Contribution

It provides the first distributed algorithm for solving distance-bounded network design convex programs with near-optimal approximation guarantees.

Findings

Runs in $O((D/\epsilon) \log n)$ rounds in the LOCAL model.

Achieves a $(1+\epsilon)$-approximation to the LP solution.

Enables improved distributed algorithms for multiple network design problems.

Abstract

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 $O( (D/\epsilon) \log n)$ rounds in the $\mathcal{LOCAL}$ model and finds a $(1+\epsilon)$-approximation to the optimal LP solution for any $0 < \epsilon \leq 1$, 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.