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Abstract. Generic programming aims to increase the flexibility of programming languages, by expanding the possibilities for parametrization — ideally, without also expanding the possibilities for uncaught errors. The term means different things to different people: parametric polymorphism, data abstraction, meta-programming, and so on. We use it to mean polytypism, that is, parametrization by the shape of data structures rather than their contents. To avoid confusion with other uses, we have coined the qualified term datatype-generic programming for this purpose. In these lecture notes, we expand on the definition of datatype-generic programming, and present some examples of datatypegeneric programs. We also explore the connection with design patterns in object-oriented programming; in particular, we argue that certain design patterns are just higher-order datatype-generic programs. 1

The "Boom hierarchy" is a hierarchy of types that begins at the level of trees and includes lists, bags and sets. This hierarchy forms the basis for the calculus of total functions developed by Bird and Meertens, and which has become known as the "BirdMeertens formalism". This paper describes a hierarchy of types that logically precedes the Boom hierarchy. We show how the basic operators of the Bird-Meertens formalism (map, reduce and filter) can be introduced in a logical sequence by beginning with a very simple structure and successively refining that structure. The context of this work is a relational theory of datatypes, rather than a calculus of total functions. Elements of the theory necessary to the later discussion are summarised at the beginning of the paper. 1 Introduction This paper reports on an experiment into the design of a programming algebra. The algebra is an algebra of datatypes oriented towards the calculation of polymorphic functions and relations. Its design d...

Functional programmers often reason about programs as if they were written in a total language, expecting the results to carry over to non-total (partial) languages. We justify such reasoning.

Polytypic programs are programs that are parameterised by type constructors (like List), unlike polymorphic programs which are parameterised by types (like Int). In this paper we formulate precisely the polytypic programming problem of "commuting " two datatypes. The precise formulation involves a novel notion of higher order polymorphism. We demonstrate via a number of examples the relevance and interest of the problem, and we show that all "regular datatypes" (the sort of datatypes that one can define in a functional programming language) do indeed commute according to our specification. The framework we use is the theory of allegories, a combination of category theory with the point-free relation calculus. 1 Polytypism The ability to abstract is vital to success in computer programming. At the macro level of requirements engineering the successful designer is the one able to abstract from the particular wishes of a few clients a general purpose product that can capture a l...

This paper is an attempt to persuade you of my viewpoint by presenting a novel generic program for a certain class of optimisation problems, named sequential decision processes. This class was originally identified by Richard Bellman in his pioneering work on dynamic programming [4]. It is a perfect example of a class of problems which are very much alike, but which has until now escaped solution by a single program. Those readers who have followed some of the work that Richard Bird and I have been doing over the last five years [6, 7] will recognise many individual examples: all of these have now been unified. The point of this observation is that even when you are on the lookout for generic programs, it can take a rather long time to discover them. The presentation below will follow that earlier work, by referring to the calculus of relations and the relational theory of data types. I shall however attempt to be light on the formalism, as I do not regard it as essential to the main thesis of this paper. Undoubtedly there are other (perhaps more convenient) notations in which the same ideas could be developed. This paper does assume some degree of familiarity with a lazy functional programming language such as Haskell, Hope, Miranda

. This paper contributes to an ongoing effort to construct a calculus for deriving programs for optimisation problems. The calculus is built around the notion of initial data types and catamorphisms which are homomorphisms on initial data types. It is shown how certain optimisation problems, which are specified in terms of a relational catamorphism, can be solved by means of a functional catamorphism. The result is illustrated with a derivation of Kruskal's algorithm for finding a minimum spanning tree in a connected graph. 1 Introduction Efficient algorithms for solving optimisation problems can sometimes be expressed as homomorphisms on initial data types. Such homomorphisms, which correspond to the familiar fold operators in functional programming, are called catamorphisms. In this paper, we give conditions under which an optimisation problem can be solved by a catamorphism. Our results are a natural generalisation of earlier work by Jeuring [5, 6], who considered the same problem ...

Given are a directed edge-labelled graph G with a distinguished node n0, and a regular expression P which may contain variables. We wish to compute all substitutions φ (of symbols for variables), together with all nodes n such that all paths n0 → n are in φ(P). We derive an algorithm for this problem using relational algebra, and show how it may be implemented in Prolog. The motivation for the problem derives from a declarative framework for specifying compiler optimisations. 1 Bob Paige and IFIP WG 2.1 Bob Paige was a long-standing member of IFIP Working Group 2.1 on Algorithmic Languages and Calculi. In recent years, the main aim of this group has been to investigate the derivation of algorithms from specifications by program transformation. Already in the mid-eighties, Bob was way ahead of the pack: instead of applying transformational techniques to well-worn examples, he was applying his theories of program transformation to new problems, and discovering new algorithms [16, 48, 52]. The secret of his success lay partly in his insistence on the study of general algorithm design strategies (in particular

Introduction Modern functional programming languages [5, 6] and specification formalisms [3] are built around the notion of inductive data types and homomorphisms on these data types. Such homomorphisms, which correspond to the familiar fold or reduce operators in functional programming, are called catamorphisms. In this note, it is shown how catamorphisms can be generalised from functions to relations, and from relations to predicate transformers. The first step of this generalisation (from functions to relations) was already achieved in a slightly different setting by Backhouse et al. [2]; the generalisation to predicate transformers is new. In practical terms, the main result presented here says that a calculus based on predicate transformers (like the refinement calculus studied by Back, Morgan and others [1, 14]) can be enriched with program constructors for iterating over inductive data types. The refineme

A discipline for the design of recursive programs is presented. The core concept