Global optimization for generalized geometric programs with mixed free-sign variables

Han Lin Li*, Hao Chun Lu

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

42 Scopus citations

Abstract

Many optimization problems are formulated as generalized geometric programming (GGP) containing signomial terms f(X) • g(Y), where X and Y are continuous and discrete free-sign vectors, respectively. By effectively convexifying f(X) and linearizing g(Y), this study globally solves a GGP with a lower number of binary variables than are used in current GGP methods. Numerical experiments demonstrate the computational efficiency of the proposed method.

Original languageEnglish
Pages (from-to)701-713
Number of pages13
JournalOperations Research
Volume57
Issue number3
DOIs
StatePublished - 05 2009
Externally publishedYes

Keywords

  • Geometric: generalized geometric programming
  • Programming

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