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Duality gaps in nonconvex stochastic optimization

Please always quote using this URN:urn:nbn:de:0296-matheon-1114
  • We consider multistage stochastic optimization models containing nonconvex constraints, e.g., due to logical or integrality requirements. We study three variants of Lagrangian relaxations and of the corresponding decomposition schemes, namely, scenario, nodal and geographical decomposition. Based on convex equivalents for the Lagrangian duals, we compare the duality gaps for these decomposition schemes. The first main result states that scenario decomposition provides a smaller or equal duality gap than nodal decomposition. The second group of results concerns large stochastic optimization models with loosely coupled components. The results provide conditions implying relations between the duality gaps of geographical decomposition and the duality gaps for scenario and nodal decomposition, respectively.

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Metadaten
Author:Darinka Dentcheva, Werner Römisch
URN:urn:nbn:de:0296-matheon-1114
Referee:Fredi Tröltzsch
Document Type:Preprint, Research Center Matheon
Language:English
Date of first Publication:2004/01/04
Release Date:2004/03/29
Preprint Number:111
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