A diagonal quadratic approximation method for large scale linear programs

John Michael Mulvey, Andrzej Ruszczyński

Research output: Contribution to journalArticlepeer-review

54 Scopus citations

Abstract

An augmented Lagrangian method is proposed for handling the common rows in large scale linear programming problems with block-diagonal structure and linking constraints. Using a diagonal quadratic approximation of the augmented Lagrangian one obtains subproblems that can be readily solved in parallel by a nonlinear primal-dual barrier method for convex separable programs. The combined augmented Lagrangian/barrier method applies in a natural way to stochastic programming and multicommodity networks.

Original languageEnglish (US)
Pages (from-to)205-215
Number of pages11
JournalOperations Research Letters
Volume12
Issue number4
DOIs
StatePublished - Jan 1 1992

All Science Journal Classification (ASJC) codes

  • Software
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Applied Mathematics

Keywords

  • decomposition
  • linear programming
  • stochastic programming

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