Improved LP-rounding approximations for the k-disjoint restricted shortest paths problem

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Abstract

Let G = (V, E) be a given (directed) graph in which every edge is with a cost and a delay that are nonnegative. The k-disjoint restricted shortest path (kRSP) problem is to compute k (edge) disjoint minimum cost paths between two distinct vertices s, t ∈ V, such that the total delay of these paths are bounded by a given delay constraint D ∈ ℝ0+. This problem is known to be NP-hard, even when k = 1 [4]. Approximation algorithms with bifactor ratio (1 + 1/r, r(1+2(log r+1)/r)(1+∈)) and (1+1/r, r(1+2(log r+1)/r)) have been developed for its special case when k = 2 respectively in [11] and [3]. For general k, an approximation algorithm with ratio (1, O(ln n)) has been developed for a weaker version of kRSP, the k bi-constraint path problem of computing k disjoint st-paths to satisfy the given cost constraint and delay constraint simultaneously [7]. In this paper, an approximation algorithm with bifactor ratio (2, 2) is first given for the kRSP problem. Then it is improved such that for any resulted solution, there exists a real number 0 ≤ α ≤ 2 that the delay and the cost of the solution is bounded, respectively, by α times and 2 - α times of that of an optimal solution. These two algorithms are both based on rounding a basic optimal solution of a LP formula, which is a relaxation of an integral linear programming (ILP) formula for the kRSP problem. The key observation of the two ratio proofs is to show that, the fractional edges of a basic solution to the LP formula will compose a graph in which the degree of every vertex is exactly 2. To the best of our knowledge, it is the first algorithm with a single factor polylogarithmic ratio for the kRSP problem. © 2014 Springer International Publishing.

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APA

Guo, L. (2014). Improved LP-rounding approximations for the k-disjoint restricted shortest paths problem. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8497 LNCS, pp. 94–104). Springer Verlag. https://doi.org/10.1007/978-3-319-08016-1_9

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