EconPapers    
Economics at your fingertips  
 

Perfect Duality in Solving Geometric Programming Problems Under Uncertainty

Dennis L. Bricker (dennis-bricker@uiowa.edu) and K. O. Kortanek (ken-kortanek@uiowa.edu)
Additional contact information
Dennis L. Bricker: University of Iowa
K. O. Kortanek: University of Iowa

Journal of Optimization Theory and Applications, 2017, vol. 173, issue 3, No 17, 1055-1065

Abstract: Abstract We examine computational solutions to all of the geometric programming problems published in a recent paper in the Journal of Optimization Theory and Applications. We employed three implementations of published algorithms interchangeably to obtain “perfect duality” for all of these problems. Perfect duality is taken to mean that a computed solution of an optimization problem achieves two properties: (1) primal and dual feasibility and (2) equality of primal and dual objective function values, all within the accuracy of the machine employed. Perfect duality was introduced by Duffin (Math Program 4:125–143,1973). When primal and dual objective values differ, we say there is a duality gap.

Keywords: Chance-constrained optimization; Geometric programming; Inventory management; Cost/profit optimization; Perfect duality; 90B30; 49K45; 90C51; 90C90 (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10957-017-1097-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joptap:v:173:y:2017:i:3:d:10.1007_s10957-017-1097-0

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10957/PS2

DOI: 10.1007/s10957-017-1097-0

Access Statistics for this article

Journal of Optimization Theory and Applications is currently edited by Franco Giannessi and David G. Hull

More articles in Journal of Optimization Theory and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla (sonal.shukla@springer.com) and Springer Nature Abstracting and Indexing (indexing@springernature.com).

 
Page updated 2024-12-28
Handle: RePEc:spr:joptap:v:173:y:2017:i:3:d:10.1007_s10957-017-1097-0