Dual-type method based algorithm for nonlinear large network optimization problems

Shin Yeu Lin, Shieh Shing Lin, Ch'i Hsin Lin

Research output: Contribution to journalJournal Article peer-review

11 Scopus citations

Abstract

In previous research, we have proposed a Dual Projected Pseudo Quasi Netwon (DPPQN) method which differs from the conventional Lagrange relaxation method by treating the inequality constraints as the domain of the primal variables in the dual function and using Projection Theory to handle the inequality constraints. We have combined this dual-type method with a Projected Jacobi (PJ) method to solve nonlinear large network optimization problems with decomposable inequality constraints, and have achieved several attractive features. To retain the attractive features and to remedy the flaw of the previous method, in the current paper, we propose an active set strategy based DPPQN method to solve the projection problem formed by coupling functional inequality constraints. This method associated with the DPPQN method and the PJ method can be used to solve general nonlinear large network optimization problems. We present this algorithm, demonstrate its computational efficiency through numerical simulations and compare it with the previous method.

Original languageEnglish
Pages (from-to)138-145
Number of pages8
JournalAsian Journal of Control
Volume4
Issue number2
DOIs
StatePublished - 06 2002
Externally publishedYes

Keywords

  • Active-set method
  • Dual method
  • Large-scale network
  • Optimization
  • Projection

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