On geometric programming problems having negative degrees of difficulty

The dual of a geometric programming problem with negative degree of difficulty is often infeasible. It has been suggested that such problems be solved by finding a dual ‘approximate’ solution which minimizes a measure of the infeasibility, e.g., the summed squares of the infeasibilities in the dual...

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Bibliographic Details
Published inEuropean journal of operational research Vol. 68; no. 3; pp. 427 - 430
Main Authors Bricker, Dennis L., Choi, Jae Chul, Rajgopal, Jayant
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 13.08.1993
Elsevier
Elsevier Sequoia S.A
SeriesEuropean Journal of Operational Research
Subjects
Applied sciences
Degeneracy
Degrees of difficulty
Duality
Exact sciences and technology
Feasibility
Geometric programming
Mathematical programming
Multivariate analysis
Nonlinear programming
Operational research and scientific management
Operational research. Management science
Operations research
Optimization
Degrees of difficulty
Degeneracy
Duality
Optimization
Geometric programming
Mathematical programming
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Summary:The dual of a geometric programming problem with negative degree of difficulty is often infeasible. It has been suggested that such problems be solved by finding a dual ‘approximate’ solution which minimizes a measure of the infeasibility, e.g., the summed squares of the infeasibilities in the dual constraints. We point out the shortcomings in that approach, and suggest a simple technique to ensure dual feasibility, namely the addition of a constant term to the primal objective.
ISSN:0377-2217
1872-6860
DOI:10.1016/0377-2217(93)90199-W