Steepest Ascent Decomposition Methods for Mathematical Programming/Economic Equilibrium Energy Planning Models

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/5134

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DC Field	Value	Language
dc.creator	Shapiro, Jeremy F., 1939-	-
dc.date	2004-05-28T19:24:41Z	-
dc.date	2004-05-28T19:24:41Z	-
dc.date	1976-02	-
dc.date.accessioned	2013-10-09T02:37:54Z	-
dc.date.available	2013-10-09T02:37:54Z	-
dc.date.issued	2013-10-09	-
dc.identifier	http://hdl.handle.net/1721.1/5134	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	A number of energy planning models have been proposed for combining econometric submodels which forecast the supply and demand for energy commodities with a linear programming submodel which optimizes the processing and transportation of these commodities. We show how convex analysis can be used to decompose these planning models into their econometric and linear programming components. Steepest ascent methods are given for optimizing the decomposition, or equivalently, for computing economic equilibria for the planning models.	-
dc.description	Supported in part by the U.S. Army Research Office (Durham) under Contract No. DAHC04-73-C-0032.	-
dc.format	1746 bytes	-
dc.format	1077535 bytes	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.publisher	Massachusetts Institute of Technology, Operations Research Center	-
dc.relation	Operations Research Center Working Paper;OR 046-76	-
dc.title	Steepest Ascent Decomposition Methods for Mathematical Programming/Economic Equilibrium Energy Planning Models	-
dc.type	Working Paper	-
Appears in Collections:	MIT Items

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