Advanced Functional Programming: 6th International School, - download pdf or read online

By Koopman P. (Editor), Plasmeijer R. (Editor), Doaitse Swierstra (Editor)

This educational e-book offers seven conscientiously revised lectures given on the sixth foreign institution on useful Programming, AFP 2008, in Heijen, The Netherlands in may well 2008.The e-book provides the next seven, rigorously cross-reviewed chapters, written through major specialists within the box: Self-adjusting: Computation with Delta ML, spider spinning for dummies, from reduction-based to reduction-free normalization, libraries for wide-spread programming in Haskell, dependently typed programming in agda, parallel and concurrent programming in Haskell and an iTask case research: a convention administration process.

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Extra resources for Advanced Functional Programming: 6th International School, AFP 2008, Heijen, The Netherlands, May 19-24, 2008, Revised Lectures

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In: Proceedings of the 23rd ACM Symposium on Computational Geometry, SCG (2007) 9. : Robust Kinetic Convex Hulls in 3D. In: Proceedings of the 16th Annual European Symposium on Algorithms (September 2008) 10. : Kinetic Algorithms via Self-Adjusting Computation. In: Proceedings of the 14th Annual European Symposium on Algorithms, September 2006, pp. 636–647 (2006) Self-adjusting Computation with Delta ML 35 11. : An experimental analysis of change propagation in dynamic trees. In: Workshop on Algorithm Engineering and Experimentation (2005) 12.

The speedups for larger inputs exceed three orders of magnitude. The speedups are significant even with relatively small input sizes. 10 Related Work The problem of having computation respond to small modifications to their data has been studied extensively in several communities. Earlier work in the programming-languages community, broadly referred to as incremental computation, focused on developing techniques for translating static/conventional programs into incremental programs that can respond automatically to modifications to their input.

It also helps address efficiently some critical robustness issues that arise in motion simulation. Previous work applies the approach to a number of problems from two [10] and three dimensions [8,9]. The solutions on three-dimensional problems made progress on well-known open problems. Other applications use self-adjusting computation to obtain dynamic algorithms for specific problems. One such application is the tree contraction algorithm of Miller and Reif [52], whose self-adjusting version provides an efficient solution to the problem of dynamic trees [7,11].

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Advanced Functional Programming: 6th International School, AFP 2008, Heijen, The Netherlands, May 19-24, 2008, Revised Lectures by Koopman P. (Editor), Plasmeijer R. (Editor), Doaitse Swierstra (Editor)


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