Augmented lagrange primal-dual approach for generalized fractional programming problems

Jen Yen Lin, Hui Ju Chen, Ruey Lin Sheu

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

In this paper, we propose a primal-dual approach for solving the generalized fractional programming problem. The outer iteration of the algo-rithm is a variant of interval-type Dinkelbach algorithm, while the augmented Lagrange method is adopted for solving the inner min-max subproblems. This is indeed a very unique feature of the paper because almost all Dinkelbach-type algorithms in the literature addressed only the outer iteration, while leaving the issue of how to practically solve a sequence of min-max subproblems un-touched. The augmented Lagrange method attaches a set of articial variables as well as their corresponding Lagrange multipliers to the min-max subprob-lem. As a result, both the primal and the dual information is available for updating the iterate points and the min-max subproblem is then reduced to a sequence of minimization problems. Numerical experiments show that the primal-dual approach can achieve a better precision in fewer iterations.

Original languageEnglish
Pages (from-to)723-741
Number of pages19
JournalJournal of Industrial and Management Optimization
Volume9
Issue number4
DOIs
Publication statusPublished - 2013 Sep 30

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All Science Journal Classification (ASJC) codes

  • Business and International Management
  • Strategy and Management
  • Control and Optimization
  • Applied Mathematics

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