Reducing the duality gap in partially convex programming
Conference
·
OSTI ID:35919
We consider the non-linear minimization program {alpha} = min{sub z{element_of}D, x{element_of}C}{l_brace}f{sub 0}(z, x) : f{sub i}(z, x) {<=} 0, i {element_of} {l_brace}1, ..., m{r_brace}{r_brace} where f{sub i}(z, {center_dot}) are convex functions, C is convex and D is compact. Following Ben-Tal, Eiger and Gershowitz we prove the existence of a partial dual program whose optimum is arbitrarily close to {alpha}. The idea, corresponds to the branching principle in Branch and Bound methods. We describe such a kind of algorithm for obtaining the desired partial dual.
- OSTI ID:
- 35919
- Report Number(s):
- CONF-9408161--
- Country of Publication:
- United States
- Language:
- English
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