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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/5079 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/5079 http://eprints.org/relation/isCoversheetVersionOf https://pure.iiasa.ac.at/id/document/5079 archive 351 disk0/00/00/74/47 2016-01-15 02:17:27 2021-08-27 17:18:54 2016-01-15 02:17:27 monograph show 3 Manne A.S. AL0975 Barreto L. Learn-by-Doing and Carbon Dioxide Abatement pub iis_ene prog_ecs Learn-by-doing model; carbon dioxide abatement; BARON There are inherent difficulties in solving learn-by-doing (LBD) 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 - particularly if there is an artful selection of the starting point or of the terminal conditions. 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 to large-scale LBD models for climate change. Meanwhile, in order to check for local optima, the most practical course may be to employ several different starting points and terminal conditions. 2004 published RP-04-013. Reprinted from Energy Economics, 26(4):621-633 [2004]. RP-04-013 Reprinted from Energy Economics; 26(4):621-633 [2004] 10 2162 public research_reprint Energy Economics 26 4 IIASA, Laxenburg, Austria 621-633 16 TRUE FALSE info:eu-repo/semantics/book