Treating Free Variables in Generalized Geometric Global Optimization Programs

Authors:
Han-Lin Li;Jung-Fa Tsai
Affiliations:
Institute of Information Management, National Chiao Tung University, Hsinchu, R.O.C 300;Department of Business Management, National Taipei University of Technology, Taipei, R.O.C 10608
Venue:
Journal of Global Optimization
Year:
2005

Citing 1
Cited 3

Deterministic Global Optimization: Theory, Methods and (NONCONVEX OPTIMIZATION AND ITS APPLICATIONS Volume 37) (Nonconvex Optimization and Its Applications)

Deterministic Global Optimization: Theory, Methods and (NONCONVEX OPTIMIZATION AND ITS APPLICATIONS Volume 37) (Nonconvex Optimization and Its Applications)

Global optimization of signomial mixed-integer nonlinear programming problems with free variables

Journal of Global Optimization
Global Optimization for Generalized Geometric Programs with Mixed Free-Sign Variables

Operations Research
A review of recent advances in global optimization

Journal of Global Optimization

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Abstract

Generalized geometric programming (GGP) problems occur frequently in engineering design and management. Recently, some exponential-based decomposition methods [Maranas and Floudas, 1997,Computers and Chemical Engineering 21(4), 351---370; Floudas et al., 1999 , Handbook of Test Problems in Local and Global Optimization, Kluwer Academic Publishers, Boston, pp. 5---105; Floudas, 2000 Deterministic Global Optimizaion: Theory, Methods and Application, Kluwer Academic Publishers, Boston, pp. 257---306] have been developed for GGP problems. These methods can only handle problems with positive variables, and are incapable of solving more general GGP problems. This study proposes a technique for treating free (i.e., positive, zero or negative) variables in GGP problems. Computationally effective convexification rules are also provided for signomial terms with three variables.