<mets:mets OBJID="eprint_4204" LABEL="Eprints Item" xsi:schemaLocation="http://www.loc.gov/METS/ http://www.loc.gov/standards/mets/mets.xsd http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-3.xsd" xmlns:mets="http://www.loc.gov/METS/" xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><mets:metsHdr CREATEDATE="2024-01-02T00:01:25Z"><mets:agent ROLE="CUSTODIAN" TYPE="ORGANIZATION"><mets:name>IIASA Repository</mets:name></mets:agent></mets:metsHdr><mets:dmdSec ID="DMD_eprint_4204_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:titleInfo><mods:title>On Augmented Lagrangian Decomposition Methods For Multistage Stochastic Programs</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">A.</mods:namePart><mods:namePart type="family">Ruszczynski</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>A general decomposition framework for large convex optimization problems based on augmented Lagrangians is described. The approach is then applied to multistage stochastic programming problems in two different ways: by decomposing the problem into scenarios or decomposing it into nodes  corresponding to stages. In both cases the method has favorable convergence  properties and a structure which makes it convenient for parallel computing  environments.</mods:abstract><mods:originInfo><mods:dateIssued encoding="iso8601">1994-02</mods:dateIssued></mods:originInfo><mods:originInfo><mods:publisher>WP-94-005</mods:publisher></mods:originInfo><mods:genre>Monograph</mods:genre></mets:xmlData></mets:mdWrap></mets:dmdSec><mets:amdSec ID="TMD_eprint_4204"><mets:rightsMD ID="rights_eprint_4204_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:useAndReproduction>
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