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

This educational ebook provides seven conscientiously revised lectures given on the sixth foreign college on sensible Programming, AFP 2008, in Heijen, The Netherlands in could 2008.The e-book provides the subsequent seven, conscientiously cross-reviewed chapters, written through best experts within the box: Self-adjusting: Computation with Delta ML, spider spinning for dummies, from reduction-based to reduction-free normalization, libraries for commonplace programming in Haskell, dependently typed programming in agda, parallel and concurrent programming in Haskell and an iTask case research: a convention administration procedure.

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

Example text

The problem of spider spinning is then to enumerate all legal colourings by starting with one such colouring and changing the colour of exactly one node at each step. As we will see, the initial colouring cannot in general be chosen to be the all-white or all-black colouring. Spider spinning has a twist: we want the generation to be loopless, meaning that the first transition should be produced in linear time in the size of the nest, and each subsequent transition in constant time. Note carefully that the idea of a loopless algorithm is defined in terms of the transitions between bit strings, not the bit strings themselves.

The idea is that every execution of the an allocation with the same key will return the same modifiable. The ΔML language relaxes the requirement about keys being unique: different allocations can share the key. In practice, it often seems to suffice for keys to act as a guide for allocation to help improve sharing between computations, but it is not necessary for them to be unique. Requiring that allocations are uniquely keyed, however, simplifies the stability analysis, which we take advantage of in our discussions of stability.

Physical location in memory). This makes stability measurements sensitive to the whims of non-deterministic memory allocation: if a program allocates memory in a way that is similar to that of the previous run, then it can be stable, if not, then it will not be stable6 . 6 Since change propagation re-executes only parts of a program, the contrast is less stark in reality. Still, non-determinism in memory allocation can detrimentally affect stability. Self-adjusting Computation with Delta ML 23 To address this unpredictability of non-deterministic memory allocation, we label each modifiable with a unique key and define two modifiables to be equal if they have the same key.

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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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