Duality gaps in nonconvex stochastic optimization
dc.contributor.author | Dentcheva, Darinka | |
dc.contributor.author | Römisch, Werner | |
dc.date.accessioned | 2017-06-15T17:44:38Z | |
dc.date.available | 2017-06-15T17:44:38Z | |
dc.date.created | 2005-11-04 | |
dc.date.issued | 2005-11-04 | |
dc.identifier.issn | 0863-0976 | |
dc.identifier.uri | http://edoc.hu-berlin.de/18452/3270 | |
dc.description.abstract | We consider multistage stochastic optimization models. Logical or integrality constraints, frequently present in optimization models, limit the application of powerful convex analysis tools. Different Lagrangian relaxation schemes and the resulting decomposition approaches provide estimates of the optimal value. We formulate convex optimization models equivalent to the dual problems of the Lagrangian relaxations. Our main results compare the resulting duality gap for these decomposition schemes. Attention is paid also to programs that model large systems with loosely coupled components. | eng |
dc.language.iso | eng | |
dc.publisher | Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, Institut für Mathematik | |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
dc.subject | Lagrangian relaxation | eng |
dc.subject | decomposition | eng |
dc.subject | Stochastic programming | eng |
dc.subject | mixed-integer | eng |
dc.subject | nonconvex | eng |
dc.subject | duality gap | eng |
dc.subject.ddc | 510 Mathematik | |
dc.title | Duality gaps in nonconvex stochastic optimization | |
dc.type | book | |
dc.identifier.urn | urn:nbn:de:kobv:11-10052526 | |
dc.identifier.doi | http://dx.doi.org/10.18452/2618 | |
local.edoc.pages | 23 | |
local.edoc.type-name | Buch | |
local.edoc.container-type | series | |
local.edoc.container-type-name | Schriftenreihe | |
local.edoc.container-year | 2002 | |
dc.identifier.zdb | 2075199-0 | |
bua.series.name | Preprints aus dem Institut für Mathematik | |
bua.series.issuenumber | 2002,5 |