A decomposition-based solution method for stochastic mixed integer nonlinear programs

This is a summary of the main results presented in the author’s PhD thesis, supervised by D. Conforti and P. Beraldi and defended on March 2005. The thesis, written in English, is available from the author upon request. It describes one of the very few existing implementations of a method for solvin...

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Veröffentlicht in:4OR Jg. 4; H. 4; S. 343 - 346
1. Verfasser: Bruni, Maria Elena
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Heidelberg Springer Nature B.V 01.12.2006
Schlagworte:
Algorithms
Global optimization
Mixed integer
Nonlinear programming
Operations research
Stochastic programming
Studies
ISSN:1619-4500, 1614-2411
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Zusammenfassung:This is a summary of the main results presented in the author’s PhD thesis, supervised by D. Conforti and P. Beraldi and defended on March 2005. The thesis, written in English, is available from the author upon request. It describes one of the very few existing implementations of a method for solving stochastic mixed integer nonlinear programming problems based on deterministic global optimization. In order to face the computational challenge involved in the solution of such multi-scenario nonconvex problems, a branch and bound approach is proposed that exploits the peculiar structure of stochastic programming problem.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1619-4500
1614-2411
DOI:10.1007/s10288-006-0007-3