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INFORMS Journal on Computing
Article . 1989 . Peer-reviewed
Data sources: Crossref
DBLP
Article . 1989
Data sources: DBLP
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Approximation Algorithms for the Maximum Concurrent Flow Problem

Approximation algorithms for the maximum concurrent flow problem
Authors: Farhad Shahrokhi;

Approximation Algorithms for the Maximum Concurrent Flow Problem

Abstract

The maximum concurrent flow problem (MCFP) is the optimization version of the feasibility problem in multicommodity flows. The objective is to maximize the percentage of the demands which is realizable for all commodities, subject to the capacity constraints. A fully polynomial ϵ-approximate algorithm was developed by Shahrokhi and Matula to solve the MCFP when the edge capacities are the same (the MCFP with uniform capacity). In this paper, we present an ϵ-approximate algorithm for the MCFP with uniform demand (when all demands are equal). Our ϵ-approximate algorithm employs a linear size reduction from the MCFP with uniform demand to the MCFP with uniform capacity and the fully polynomial ϵ-approximate algorithm for the MCFP with uniform capacity. The computational results indicate that the algorithm is efficient. We also present an efficient combinatorial algorithm for the MCFP in planar graphs. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

Related Organizations
Keywords

linear size reduction, Computational methods for problems pertaining to operations research and mathematical programming, Deterministic network models in operations research, maximum concurrent flow problem, fully polynomial \(\varepsilon\)-approximate algorithm, multicommodity flows

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Average
Top 10%
Average
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