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Simple Generalized Maximum Flow Algorithms

  • Éva Tardos
  • Kevin D. Wayne
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1412)

Abstract

We introduce a gain-scaling technique for the generalized maximum flow problem. Using this technique, we present three simple and intuitive polynomial-time combinatorial algorithms for the problem. Truemper’s augmenting path algorithm is one of the simplest combi- natorial algorithms for the problem, but runs in exponential-time. Our first algorithm is a polynomial-time variant of Truemper’s algorithm. Our second algorithm is an adaption of Goldberg and Tarjan’s preflow- push algorithm. It is the first polynomial-time preflow-push algorithm in generalized networks. Our third algorithm is a variant of the Fat-Path capacity-scaling algorithm. It is much simpler than Radzik’s variant and matches the best known complexity for the problem. We discuss practical improvements in implementation.

Keywords

Generalize Maximum Residual Network Canonical Label Lossy Network Negative Cost Cycle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Éva Tardos
    • 1
    • 2
  • Kevin D. Wayne
    • 2
  1. 1.Computer Science DepartmentCornell UniversityIthacaUSA
  2. 2.Operations Research and Industrial Engineering DepartmentCornell UniversityIthacaUSA

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