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
