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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/4744 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/4744 http://eprints.org/relation/isCoversheetVersionOf https://pure.iiasa.ac.at/id/document/4744 archive 351 disk0/00/00/64/67 2016-01-15 02:13:38 2021-08-27 17:17:33 2016-01-15 02:13:38 monograph show 2 Manne A.S. AL0975 Barreto L. pub iis_ene iis_env iis_mod prog_ecs There are inherent difficulties in solving LBD(learn-by-doing) models. Basic to such models is the idea that the accumulation of experience leads to a lowering of costs. This paper is intended to explore some of the algorithmic issues in LBD modeling for carbon dioxide abatement. When using a standard algorithm for nonlinear programming, there is no guarantee that a local LBD optimum will also be a global optimum. Fortunately, despite the absence of guarantees, there is a good chance that one of the standard algorithms will produce a global optimum for models of this type. Moreover, there is a new procedure named BARON. In the case of small models, a global optimum can be recognized and guaranteed through BARON. Eventually, it should be possible for BARON or a similar approach to be extended top large-scale LBD models for climate change. Meanwhile, in order to check for local optima, the most practical course is to apply several different nonlinear programming algorithms - and several different starting solutions with each of them. 2001-11 published IR-01-057 IR-01-057 2162 public interim_report IIASA, Laxenburg, Austria 27 FALSE info:eu-repo/semantics/book