Efficient Convexification Strategy for Generalized Geometric Programming Problems | Zendy

HaoChun Lu | Zendy; Liming Yao | Zendy

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Efficient Convexification Strategy for Generalized Geometric Programming Problems

Author(s) -

HaoChun Lu,

Liming Yao

Publication year - 2019

Publication title -

informs journal on computing

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.403

H-Index - 80

eISSN - 1526-5528

pISSN - 1091-9856

DOI - 10.1287/ijoc.2018.0850

Subject(s) - transformation (genetics) , geometric programming , mathematical optimization , mathematics , regular polygon , computation , convex optimization , computer science , algorithm , biochemistry , chemistry , geometry , gene

Generalized geometric programming (GGP) problems consist of a signomial being minimized in the objective function subject to signomial constraints, and such problems have been utilized in various fields. After modeling numerous applications as GGP problems, solving them has become a significant requirement. A convex underestimator is considered an important concept to solve GGP problems for obtaining the global minimum. Among convex underestimators, variable transformation is one of the most popular techniques. This study utilizes an estimator to solve the difficulty of selecting an appropriate transformation between the exponential transformation and power convex transformation techniques and considers all popular types of transformation techniques to develop a novel and efficient convexification strategy for solving GGP problems. This proposed convexification strategy offers a guide for selecting the most appropriate transformation techniques on any condition of a signomial term to obtain the tightest c...

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