By Umut A. Acar, Ruy Ley-Wild (auth.), Pieter Koopman, Rinus Plasmeijer, Doaitse Swierstra (eds.)
This instructional publication provides seven conscientiously revised lectures given on the sixth overseas university on practical Programming, AFP 2008, in Heijen, The Netherlands in may well 2008.
The booklet provides the subsequent seven, conscientiously cross-reviewed chapters, written through prime experts within the box: Self-adjusting: Computation with Delta ML, spider spinning for dummies, from reduction-based to reduction-free normalization, libraries for favourite programming in Haskell, dependently typed programming in agda, parallel and concurrent programming in Haskell and an iTask case research: a convention administration system.
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Extra resources for Advanced Functional Programming: 6th International School, AFP 2008, Heijen, The Netherlands, May 2008, Revised Lectures
Incremental reduction in the lambda calculus. In: Proceedings of the ACM 1990 Conference on LISP and Functional Programming, June 1990, pp. 307–322 (1990) 33. : Incremental Reduction in the Lambda Calculus and Related Reduction Systems. PhD thesis, Department of Computer Science, November 1991. Cornell University (1991) 34. : Data structures for on-line updating of minimum spanning trees, with applications. SIAM Journal on Computing 14, 781–798 (1985) 35. : A data structure for dynamically maintaining rooted trees.
These properties make them difﬁcult to adapt to diﬀerent problems, implement, and use in practice. Algorithms researchers also study a closely related class of data structures, called kinetic data structures, for performing motion simulations eﬃciently . A. Acar and R. Ley-Wild continuous motion (Section 2) by updating computed properties eﬃciently. , [15,36] for surveys). These data structures share many characteristics of dynamic data structures. They also pose additional implementation challenges [15,37,60,59], due to diﬃculties with motion modeling and handling of numerical errors.
A putter takes two arguments: a key associated with the modiﬁable to be allocated and a value to place into the allocated modiﬁable. We often create putters locally for each function. Since each putter has its own hash function, creating local putters helps eliminate key collision between diﬀerent functions: allocations performed by diﬀerent putters can share the same keys without causing a collision. 8 Modifiable Lists We now turn to the implementation of lists and their asymptotic complexity under change propagation.
Advanced Functional Programming: 6th International School, AFP 2008, Heijen, The Netherlands, May 2008, Revised Lectures by Umut A. Acar, Ruy Ley-Wild (auth.), Pieter Koopman, Rinus Plasmeijer, Doaitse Swierstra (eds.)