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