Identifying the Optimal Face of a Network Linear Program with a Globally Convergent Interior Point Method

Mauricio G. C. Resende, Takashi Tsuchiya and Geraldo Veiga
Additional contact information
Mauricio G. C. Resende: AT&T Bell Laboratories
Takashi Tsuchiya: The Institute of Statistical Mathematics
Geraldo Veiga: University of California

A chapter in Large Scale Optimization, 1994, pp 362-387 from Springer

Abstract: Abstract Based on recent convergence results for the affine scaling algorithm for linear programming, we investigate strategies to identify the optimal face of a minimum cost network flow problem. In the computational experiments described, one of the proposed optimality indicators is used to implement an early stopping criterion in DLNET, an implementation of the dual affine scaling algorithm for solving minimum cost network flow problems. We conclude from the experiments that the new indicator is far more robust than the one used in earlier versions of DLNET.

Keywords: Linear programming; minimum cost network flow; indicator; affine scaling algorithm; computer implementation (search for similar items in EconPapers)
Date: 1994
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4613-3632-7_18

Ordering information: This item can be ordered from
http://www.springer.com/9781461336327

DOI: 10.1007/978-1-4613-3632-7_18

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().