By Koopman P. (Editor), Plasmeijer R. (Editor), Doaitse Swierstra (Editor)
This instructional publication offers seven conscientiously revised lectures given on the sixth overseas institution on sensible Programming, AFP 2008, in Heijen, The Netherlands in may perhaps 2008.The ebook offers the next seven, rigorously cross-reviewed chapters, written by way of best professionals within the box: Self-adjusting: Computation with Delta ML, spider spinning for dummies, from reduction-based to reduction-free normalization, libraries for normal programming in Haskell, dependently typed programming in agda, parallel and concurrent programming in Haskell and an iTask case examine: a convention administration approach.
Read Online or Download Advanced Functional Programming: 6th International School, AFP 2008, Heijen, The Netherlands, May 19-24, 2008, Revised Lectures PDF
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Additional resources for Advanced Functional Programming: 6th International School, AFP 2008, Heijen, The Netherlands, May 19-24, 2008, Revised Lectures
Carnegie Mellon University (May 2005) 3. : Imperative self-adjusting computation. In: Proceedings of the 25th Annual ACM Symposium on Principles of Programming Languages (2008) 4. : An experimental analysis of self-adjusting computation. In: Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (2006) 5. : Adaptive Functional Programming. In: Proceedings of the 29th Annual ACM Symposium on Principles of Programming Languages, pp. 247–259 (2002) 6. : Selective memoization.
Thus, we can use modiﬁable lists to represent a set that changes over time. As with conventional lists, we can write various primitive list functions. Here we describe how to implement three functions on lists: lengthLessThan, map, and reduce. Omitting the self-adjusting primitives, the ﬁrst two functions are nearly identical to their conventional counterparts and therefore have comparable asymptotic complexity; the complexity of reduce is discussed below. In our implementation, we observe the following convention.
Another example is Minimum Spanning Trees (MST), whose dynamic version has required more than a decade of research to solve eﬃciently [34,31,41,40,44], while its static/conventional version is straightforward. Other examples include the problem of dynamic trees, whose various ﬂavors have been studied extensively [63,64,26,56,40,68,17,35,18,67,66]. Because dynamic algorithms are designed to support a particular set of modiﬁcations, they are highly specialized (an algorithm may be eﬃcient for some modiﬁcations to data but not others), naturally more complex than their static versions, and are not composable (Section 2).