SearcharxivSearch

arXiv subjects

Julian Enoch

Publications and source records attributed to Julian Enoch.

2 recordsLinked to original sources

Faster Algorithm for Maximum Flow in Directed Planar Graphs with Vertex Capacities

We give an $O(k^3 n \log n \min(k,\log^2 n) \log^2(nC))$-time algorithm for computing maximum integer flows in planar graphs with integer arc {\em and vertex} capacities bounded by $C$, and $k$ sources and sinks. This improves by a factor of $\max(k^2,k\log^2 n)$ over the fastest algorithm previously known for this problem [Wang, SODA 2019]. The speedup is obtained by two independent ideas. First we replace an iterative procedure of Wang that uses $O(k)$ invocations of an $O(k^3 n \log^3 n)$-time algorithm for maximum flow algorithm in a planar graph with $k$ apices [Borradaile et al., FOCS 2012, SICOMP 2017], by an alternative procedure that only makes one invocation of the algorithm of Borradaile et al. Second, we show two alternatives for computing flows in the $k$-apex graphs that arise in our modification of Wang's procedure faster than the algorithm of Borradaile et al. In doing so, we introduce and analyze a sequential implementation of the parallel highest-distance push-relabel algorithm of Goldberg and Tarjan~[JACM 1988].

cs.DS

Nested Column Generation decomposition for solving the Routing and Spectrum Allocation problem in Elastic Optical Networks

With the continued growth of Internet traffic, and the scarcity of the optical spectrum, there is a continuous need to optimize the usage of this resource. In the process of provisioning elastic optical networks using the flexible frequency grid paradigm, telecommunication operators must deal with a combinatorial optimization problem that is NP-complete namely the Routing and Spectrum Allocation(RSA) problem. Following on our previous study, where we used Integer Linear Programming, and proposed a Column Generation algorithm based on a Lightpath decomposition, which proved to be the most efficient so far, we now consider the traditional Configuration decomposition that has been studied in other works in the past. In the process, we created an new mathematical model using two variable sets instead of a single variable set. Equally important,we independently rediscovered the Nested Column Generation technique, and we used it to propose an algorithm that led to a considerable improvement on the previous algorithms that use the same Configuration decomposition. When compared to the latest such existing study, our algorithm achieved an accuracy gap of 1% as opposed to 14.3% for the previous study, and a running time two orders of magnitude faster on average.

cs.NI