eprintid: 13586
rev_number: 8
eprint_status: archive
userid: 353
dir: disk0/00/01/35/86
datestamp: 2016-08-04 12:44:05
lastmod: 2021-08-27 17:41:25
status_changed: 2016-08-04 12:44:05
type: article
metadata_visibility: show
item_issues_count: 1
creators_name: Birge, J.
creators_name: Rosa, C.
creators_id: 1545
title: Parallel decomposition of large-scale stochastic nonlinear programs
ispublished: pub
keywords: Decomposition; Economics; Environment; Parallel computation; Stochastic programming
abstract: Many practical decision problems involve both nonlinear relationships and uncertainties. The resulting stochastic nonlinear programs become quite difficult to solve as the number of possible scenarios increases. In this paper, we provide a decomposition method for problems in which nonlinear constraints appear within periods. We also show how the method extends to lower bounding refinements of the set of scenarios when the random data are independent from period to period. We then apply the method to a stochastic model of the U.S. economy based on the Global 2100 method developed by Manne and Richels.
date: 1996-12
date_type: published
publisher: Springer
id_number: 10.1007/BF02187640
creators_browse_id: 1541
full_text_status: none
publication: Annals of Operations Research
volume: 64
number: 1
pagerange: 39-65
refereed: TRUE
issn: 0254-5330
coversheets_dirty: FALSE
fp7_project: no
fp7_type: info:eu-repo/semantics/article
citation:   Birge, J. & Rosa, C. <https://pure.iiasa.ac.at/view/iiasa/1541.html>  (1996).  Parallel decomposition of large-scale stochastic nonlinear programs.   Annals of Operations Research 64 (1) 39-65. 10.1007/BF02187640 <https://doi.org/10.1007/BF02187640>.