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Robust Geometric Programming is co-NP hard

  • Geometric Programming is a useful tool with a wide range of applications in engineering. As in real-world problems input data is likely to be affected by uncertainty, Hsiung, Kim, and Boyd introduced robust geometric programming to include the uncertainty in the optimization process. They also developed a tractable approximation method to tackle this problem. Further, they pose the question whether there exists a tractable reformulation of their robust geometric programming model instead of only an approximation method. We give a negative answer to this question by showing that robust geometric programming is co-NP hard in its natural posynomial form.

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Metadaten
Author:André Chassein, Marc Goerigk
URN (permanent link):urn:nbn:de:hbz:386-kluedo-39380
Document Type:Preprint
Language of publication:English
Publication Date:2014/12/04
Year of Publication:2014
Publishing Institute:Technische Universität Kaiserslautern
Date of the Publication (Server):2014/12/05
Number of page:6
Faculties / Organisational entities:Fachbereich Mathematik
DDC-Cassification:5 Naturwissenschaften und Mathematik / 510 Mathematik
Licence (German):Standard gemäß KLUEDO-Leitlinien vom 28.10.2014