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

Han Lin Li; Hao Chun Lu

doi:10.1287/opre.1080.0586

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

Han Lin Li^*
, Hao Chun Lu

^*Corresponding author for this work

National Yang Ming Chiao Tung University

Research output: Contribution to journal › Journal Article › peer-review

43 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 language	English
Pages (from-to)	701-713
Number of pages	13
Journal	Operations Research
Volume	57
Issue number	3
DOIs	https://doi.org/10.1287/opre.1080.0586
State	Published - 05 2009
Externally published	Yes

Keywords

Geometric: generalized geometric programming
Programming

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10.1287/opre.1080.0586

Cite this

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title = "Global optimization for generalized geometric programs with mixed free-sign variables",

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.",

keywords = "Geometric: generalized geometric programming, Programming",

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AU - Lu, Hao Chun

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AB - 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.

KW - Geometric: generalized geometric programming

KW - Programming

UR - https://www.scopus.com/pages/publications/68149178263

U2 - 10.1287/opre.1080.0586

DO - 10.1287/opre.1080.0586

M3 - 文章

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SP - 701

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JO - Operations Research

JF - Operations Research

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

Abstract

Keywords

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