A principled approach to programming with nested types in Haskell

Initial algebra semantics is one of the cornerstones of the theory of modern functional programming languages. For each inductive data type, it provides a Church encoding for that type, a build combinator which constructs data of that type, a fold combinator which encapsulates structured recursion over data of that type, and a fold/build rule which optimises modular programs by eliminating from them data constructed using the buildcombinator, and immediately consumed using the foldcombinator, for that type. It has long been thought that initial algebra semantics is not expressive enough to provide a similar foundation for programming with nested types in Haskell. Specifically, the standard folds derived from initial algebra semantics have been considered too weak to capture commonly occurring patterns of recursion over data of nested types in Haskell, and no build combinators or fold/build rules have until now been defined for nested types. This paper shows that standard folds are, in fact, sufficiently expressive for programming with nested types in Haskell. It also defines buildcombinators and fold/build fusion rules for nested types. It thus shows how initial algebra semantics provides a principled, expressive, and elegant foundation for programming with nested types in Haskell

Parberry’s pairwise sorting network revealed

Batcher’s “merge exchange” sorting network, discussed in a previous pearl (Hinze & Martin, 2016), remains one of the best practical algorithms for oblivious sorting, even almost half a century after its inception. So it is surprising that an algorithm with exactly the same level of performance, devised two decades later by Parberry (1992), has been relatively overlooked. Perhaps a reason for its lack of celebrity is that Parberry’s design is not immediately recognizable, whereas the Batcher method has a familiar ring, as a hardwired implementation of merge sort. Here we hope to rectify this imbalance by unravelling Parberry’s algorithm and uncoupling its close relationship to Batcher’s. Interestingly, Parberry derives his network using the zero-one principle (Knuth, 1998). We abandon this traditional method, in favour of a feature of comparison networks that we consider to be more fundamental: monotonicity. We shall see that this property, used before to demystify Batcher’s merger (Hinze & Martin, 2016), also helps to shed some light on Parberry’s design. To keep the pearl reasonably self-contained we start with a quick recap of the notation and Batcher’s construction

Set-theoretic Types for Erlang

Erlang is a functional programming language with dynamic typing. The language offers great flexibility for destructing values through pattern matching and dynamic type tests. Erlang also comes with a type language supporting parametric polymorphism, equi-recursive types, as well as union and a limited form of intersection types. However, type signatures only serve as documentation, there is no check that a function body conforms to its signature. Set-theoretic types and semantic subtyping fit Erlang's feature set very well. They allow expressing nearly all constructs of its type language and provide means for statically checking type signatures. This article brings set-theoretic types to Erlang and demonstrates how existing Erlang code can be statically typechecked without or with only minor modifications to the code. Further, the article formalizes the main ingredients of the type system in a small core calculus, reports on an implementation of the system, and compares it with other static typecheckers for Erlang.Comment: 14 pages, 9 figures, IFL 2022; latexmk -pdf to buil

Polytypic Functions Over Nested Datatypes

The theory and practice of polytypic programming is intimately connected with the initial algebra semantics of datatypes. This is both a blessing and a curse. It is a blessing because the underlying theory is beautiful and well developed. It is a curse because the initial algebra semantics is restricted to so-called regular datatypes. Recent work by R. Bird and L. Meertens [3] on the semantics of non-regular or nested datatypes suggests that an extension to general datatypes is not entirely straightforward. Here we propose an alternative that extends polytypism to arbitrary datatypes, including nested datatypes and mutually recursive datatypes. The central idea is to use rational trees over a suitable set of functor symbols as type arguments for polytypic functions. Besides covering a wider range of types the approach is also simpler and technically less involving than previous ones. We present several examples of polytypic functions, among others polytypic reduction and polytypic equality. The presentation assumes some background in functional and in polytypic programming. A basic knowledge of monads is required for some of the examples

Proceedings of the Workshop on Algorithmic Aspects of Advanced Programming Languages: WAAAPL'99: Paris, France, September 30, 1999

The first Workshop on Algorithmic Aspects of Advanced Programming Languages was held on September 30, 1999, in Paris, France, in conjunction with the PLI'99 conferences and workshops. The choice of programming languages has a huge effect on the algorithms and data structures that are to be implemented in that language. Traditionally, algorithms and data structures have been studied in the context of imperative languages. This workshop considers the algorithmic implications of choosing an advanced functional or logic programming language instead. A total of eight papers were selected for presentation at the workshop, together with an invited lecture by Robert Harper. We would like to thank Dider Remv, general chair of PLI'99, for his assistance in organizing this workshop

Perfect Trees and Bit-reversal Permutations

A famous algorithm is the Fast Fourier Transform, or FFT. An efficient iterative version of the FFT algorithm performs as a first step a bit-reversal permutation of the input list. The bit-reversal permutation swaps elements whose indices have binary representations that are the reverse of each other. Using an amortized approach this operation can be made to run in linear time on a random-access machine. An intriguing question is whether a linear-time implementation is also feasible on a pointer machine, that is in a purely functional setting. We show that the answer to this question is in the affirmative. In deriving a solution we employ several advanced programming language concepts such as nested datatypes, associated fold and unfold operators, rank-2 types, and polymorphic recursion. 1 Introduction A bit-reversal permutation operates on lists whose length is n = 2 k for some natural number k and swaps elements whose indices have binary representations that are the reverse of eac..

Perfect Trees and Bit-Reversal Permutations

A famous algorithm is the Fast Fourier Transform, or FFT. An ecient iterative version of the FFT algorithm performs as a rst step a bit-reversal permutation of the input list. The bit-reversal permutation swaps elements whose indices have binary representations that are the reverse of each other. Using an amortized approach this operation can be made to run in linear time on a random-access machine. An intriguing question is whether a linear-time implementation is also feasible on a pointer machine, that is in a purely functional setting. We show that the answer to this question is in the armative. In deriving a solution we employ several advanced programming language concepts such as nested datatypes, associated fold and unfold operators, rank-2 types, and polymorphic recursion

Functional Pearl: Perfect trees and bit−reversal permutations

A famous algorithm is the Fast Fourier Transform, or FFT. An efficient iterative version of the FFT algorithm performs as a first step a bit-reversal permutation of the input list. The bit-reversal permutation swaps elements whose indices have binary representations that are the reverse of each other. Using an amortized approach this operation can be made to run in linear time on a random-access machine. An intriguing question is whether a linear-time implementation is also feasible on a pointer machine, that is in a purely functional setting. We show that the answer to this question is in the affirmative. In deriving a solution we employ several advanced programming language concepts such as nested datatypes, associated fold and unfold operators, rank-2 types, and polymorphic recursion

Functional Pearl: Perfect trees and bit−reversal permutations

A famous algorithm is the Fast Fourier Transform, or FFT. An efficient iterative version of the FFT algorithm performs as a first step a bit-reversal permutation of the input list. The bit-reversal permutation swaps elements whose indices have binary representations that are the reverse of each other. Using an amortized approach this operation can be made to run in linear time on a random-access machine. An intriguing question is whether a linear-time implementation is also feasible on a pointer machine, that is in a purely functional setting. We show that the answer to this question is in the affirmative. In deriving a solution we employ several advanced programming language concepts such as nested datatypes, associated fold and unfold operators, rank-2 types, and polymorphic recursion