@article {19590,
	title = {Optimizing Abstract Abstract Machines},
	journal = {arXiv:1211.3722 [cs]},
	year = {2013},
	note = {Comment: Proceedings of the International Conference on Functional Programming 2013 (ICFP 2013). Boston, Massachusetts. September, 2013},
	month = {2013///},
	abstract = {The technique of abstracting abstract machines (AAM) provides a systematic approach for deriving computable approximations of evaluators that are easily proved sound. This article contributes a complementary step-by-step process for subsequently going from a naive analyzer derived under the AAM approach, to an efficient and correct implementation. The end result of the process is a two to three order-of-magnitude improvement over the systematically derived analyzer, making it competitive with hand-optimized implementations that compute fundamentally less precise results.},
	keywords = {Computer Science - Programming Languages, F.3.2},
	url = {http://arxiv.org/abs/1211.3722},
	author = {Johnson, J. Ian and Labich, Nicholas and Might, Matthew and David Van Horn}
}
