An ε-Relaxation method for generalized separable convex cost network flow problems

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We propose an extension of the ε-relaxation method to generalized network flow problems with separable convex cost. The method maintains ε-complementary slackness satisfied at all iterations and adjusts the arc flows and the node prices so to satisfy flow conservation upon termination. Each iteration of the method involves either a price change at a node or a flow change at an arc or a flow change around a simple cycle. Complexity bounds for the method are derived. For one implementation employing ε-scaling, the bound is polynomial in the number of nodes N, the number of arcs A, a certain constant Γ depending on the arc gains, and ln(ε0/ε), where ε0 and ε denote, respectively, the initial and the final ε.

Original languageEnglish (US)
Title of host publicationInteger Programming and Combinatorial Optimization - 5th International IPCO Conference, 1996 Proceedings
EditorsWilliam H. Cunningham, S.Thomas McCormick, Maurice Queyranne
PublisherSpringer Verlag
Pages85-93
Number of pages9
ISBN (Print)3540613102, 9783540613107
DOIs
StatePublished - 1996
Externally publishedYes
Event5th International Conference Integer Programming and Combinatorial Optimization, IPCO 1996 - Vancouver, Canada
Duration: Jun 3 1996Jun 5 1996

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1084
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Conference Integer Programming and Combinatorial Optimization, IPCO 1996
CountryCanada
CityVancouver
Period6/3/966/5/96

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'An ε-Relaxation method for generalized separable convex cost network flow problems'. Together they form a unique fingerprint.

Cite this