In this paper we show that it is possible to implement a symmetric set of finite-list operations efficiently; the set is symmetric in the sense that lists can be manipulated at either end. We derive the definitions of these operations from their specifications by calculation. The operations have O(1) time complexity, provided that we content ourselves with, so-called, amortized efficiency, instead of worst-case efficiency

In this article, we extend the Barendregt Cube with ∏-conversion (which is the analogue of β-conversion, on product type level) and study its properties. We use this extension to separate the problem of whether a term is typable from the problem of what is the type of a term.

Injective pure type systems form a large class of pure type systems for which one can compute by purely syntactic means two sorts elmt(Γ[mid ]M) and sort(Γ[mid ]M), where Γ is a pseudo-context and M is a pseudo-term, and such that for every sort s,

formula here

By eliminating the problematic clause in the (abstraction) rule in favor of constraints over elmt(.[mid ].) and sort(.[mid ].), we provide a sound and complete type-checking algorithm for injective pure type systems. In addition, we prove expansion postponement for a variant of injective pure type systems where the problematic clause in the (abstraction) rule is replaced in favor of constraints over elmt(.[mid ].) and sort(.[mid ].).

The purpose of this paper is to demonstrate that first-order functional programs can be transformed into intensional programs of nullary variables, in a semantics preserving way. On the foundational side, the goal of our study is to bring new insights and a better understanding of the nature of functional languages. From a practical point of view, our investigation provides a formal basis for the tagging mechanism that is used in the implementation of first-order functional languages on dataflow machines.

Nicholas Pippenger has recently given a problem that, under two simple restrictions, can be solved in linear time by an impure Lisp program, but requires Ω(n log n) steps to be solved by any eager pure Lisp program. By showing how to solve the problem in linear time with a lazy functional program, we demonstrate that – for some problems at least – lazy evaluators are strictly more powerful than eager ones.

This article describes the application of functional programming techniques to a problem previously studied by imperative programmers, that of drawing general trees automatically. We first consider the nature of the problem and the ideas behind its solution (due to Radack), independent of programming language implementation. We then describe a Standard ML program which reflects the structure of the abstract solution much better than an imperative language implementation. We conclude with an informal discussion on the correctness of the implementation and some changes which improve the algorithm's worst-case time complexity.

A lambda-free logical framework takes parameterisation and definitions as the basic notions to provide schematic mechanisms for specification of type theories and their use in practice. The framework presented here, PAL+, is a logical framework for specification and implementation of type theories, such as Martin-Löf's type theory or UTT. As in Martin-Löf's logical framework (Nordström et al., 1990), computational rules can be introduced and are used to give meanings to the declared constants. However, PAL+ only allows one to talk about the concepts that are intuitively in the object type theories: types and their objects, and families of types and families of objects of types. In particular, in PAL+, one cannot directly represent families of families of entities, which could be done in other logical frameworks by means of lambda abstraction. PAL+ is in the spirit of de Bruijn's PAL+ for Automath (de Bruijn, 1980). Compared with PAL, PAL+ allows one to represent parametric concepts such as families of types and families of non-parametric objects, which can be used by themselves as totalities as well as when they are fully instantiated. Such parametric objects are represented by local definitions (let-expressions). We claim that PAL+ is a correct meta-language for specifying type theories (e.g., dependent type theories), as it has the advantage of exactly capturing the intuitive concepts in object type theories, and that its implementation reflects the actual use of type theories in practice. We shall study the meta-theory of PAL+ by developing its typed operational semantics and showing that it has nice meta-theoretic properties.

Design and quality are fundamental themes in engineering education. Functionalprogramming builds software from small components, a central element of gooddesign, and facilitates reasoning about correctness, an important aspect ofquality. Software engineering courses that employ functional programming providea platform for educating students in the design of quality software. This pearldescribes experiments in the use of ACL2, a purely functional subset of CommonLisp with an embedded mechanical logic, to focus on design and correctness insoftware engineering courses. Students find the courses challenging andinteresting. A few acquire enough skill to use an automated theorem prover onthe job without additional training. Many students, but not quite a majority,find enough success to suggest that additional experience would make themeffective users of mechanized logic in commercial software development. Nearlyall gain a new perspective on what it means for software to be correct andacquire a good understanding of functional programming.

In this paper we present two type inference systems for detecting useless-code in higher-order typed functional programs. Type inference can be performed in an efficient and complete way, by reducing it to the solution of a system of constraints. We also give a useless-code elimination algorithm which is based on a combined use of these type inference systems. The main application of the technique is the optimization of programs extracted from proofs in logical frameworks, but it could be used as well in the elimination of useless-code determined by program transformations.

Some extensions of the basic formalism of stream processing functions are useful to specify complex structures such as operating systems. In this paper we give the foundations of higher order stream processing functions. These are functions which send and accept not only messages representing atomic data, but also complex elements such as functions. Some special notations are introduced for the specification and manipulation of such functions. A representation of time is outlined, which enables us to model time dependent behaviour. Finally, we demonstrate how characteristic operating system structures can be modelled by timed higher order stream processing functions.

Suppose, you want to implement a structured editor for some term type, so that the user can navigate through a given term and perform edit actions on subterms. In this case you are immediately faced with the problem of how to keep track of the cursor movements and the user's edits in a reasonably efficient manner. In a previous pearl, Huet (1997) introduced a simple data structure, the Zipper, that addresses this problem – we will explain the Zipper briefly in section 2. A drawback of the Zipper is that the type of cursor locations depends on the structure of the term type, i.e. each term type gives rise to a different type of location (unless you are working in an untyped environment). In this pearl, we present an alternative data structure, the web, that serves the same purpose, but that is parametric in the underlying term type. Sections 3–6 are devoted to the new data structure. Before we unravel the Zipper and explore the web, let us first give a taste of their use.

The Bee is an integrated development environment for the Scheme programming language. It provides the user with a connection between Scheme and the C programming language, a symbolic debugger, a profiler, an interpreter, an optimizing compiler that delivers stand alone executables, a source file browser, a project manager, user libraries and online documentation. This article details the facilities of the Bee, its user interface, and presents an overview of the implementation of its main components.

Automatic differentiation (AD) is a technique for augmenting computer programs to compute derivatives. The essence of AD in its forward accumulation mode is to attach perturbations to each number, and propagate these through the computation by overloading the arithmetic operators. When derivatives are nested, the distinct derivative calculations, and their associated perturbations, must be distinguished. This is typically accomplished by creating a unique tag for each derivative calculation and tagging the perturbations. We exhibit a subtle bug, present in fielded implementations which support derivatives of higher-order functions, in which perturbations are confused despite the tagging machinery, leading to incorrect results. The essence of the bug is as follows: a unique tag is needed for each derivative calculation, but in existing implementations unique tags are created when taking the derivative of a function at a point. When taking derivatives of higher-order functions, these need not correspond! We exhibit a simple example: a higher-order function f whose derivative at a point x, namely f′(x), is itself a function which calculates a derivative. This situation arises naturally when taking derivatives of curried functions. Two potential solutions are presented, and their deficiencies discussed. One uses eta expansion to delay the creation of fresh tags in order to put them into one-to-one correspondence with derivative calculations. The other wraps outputs of derivative operators with tag substitution machinery. Both solutions seem very difficult to implement without violating the desirable complexity guarantees of forward AD.

In a functional program, a simple random number generator may generate a lazy list of random numbers. This is fine when the random numbers are consumed sequentially at a single point in the program. However, things are more complicated in a program where random numbers are used at many locations, such as in a large simulation. The programmer should not need to worry about providing separate generators with a unique seed at each point where random numbers are used. At the same time, the programmer should not need to coordinate the use of a single stream of random numbers in many parts of the program, which can be particularly difficult with lazy evaluation or parallel processing.

We discuss several techniques for distributing random numbers to various parts of a program, and some methods of allowing different program components to evaluate random numbers locally. We then propose a new approach in which a random number sequence can be split at a random point to produce a pair of random number sequences that can be used independently at different points in the computation.

The approach can also be used in distributed procedural programs, where it is desirable to avoid dealing with a single source of random numbers. The approach has the added advantage of producing repeatable results, as might be needed in debugging, for example.

In the static analysis of functional programs, pushdown flow analysis and abstract garbage collection push the boundaries of what we can learn about programs statically. This work illuminates and poses solutions to theoretical and practical challenges that stand in the way of combining the power of these techniques. Pushdown flow analysis grants unbounded yet computable polyvariance to the analysis of return-flow in higher-order programs. Abstract garbage collection grants unbounded polyvariance to abstract addresses which become unreachable between invocations of the abstract contexts in which they were created. Pushdown analysis solves the problem of precisely analyzing recursion in higher-order languages; abstract garbage collection is essential in solving the “stickiness” problem. Alone, our benchmarks demonstrate that each method can reduce analysis times and boost precision by orders of magnitude. We combine these methods. The challenge in marrying these techniques is not subtle: computing the reachable control states of a pushdown system relies on limiting access during transition to the top of the stack; abstract garbage collection, on the other hand, needs full access to the entire stack to compute a root set, just as concrete collection does. Conditional pushdown systems were developed for just such a conundrum, but existing methods are ill-suited for the dynamic nature of garbage collection. We show fully precise and approximate solutions to the feasible paths problem for pushdown garbage-collecting control-flow analysis. Experiments reveal synergistic interplay between garbage collection and pushdown techniques, and the fusion demonstrates “better-than-both-worlds” precision.

The Functional Payout Framework (fpf) is a Haskell application that uses an embedded domain-specific functional language to represent and process exotic financial derivatives. Whereas scripting languages for pricing exotic derivatives are common in banking, fpf uses multiple interpretations to not only price such trades, but also to analyse the scripts to provide lifecycle support and more. This paper discusses fpf in relation to the wider trading workflow and our experiences in using a functional language in such a system as both an implementation language and a domain-specific language.

In this paper, we show how lazy functional programs can be compiled for the Java Virtual Machine using a mapping between a version of the 〈v, G〉-machine and the Java Virtual Machine. This mapping is elegant – the description is entirely straightforward – and efficient – using it, both code size and execution speed are of the same order of magnitude as those obtained with a traditional functional language bytecode interpreter. In future, our work could serve as the basis of an interface between Haskell and Java.

A higher-order function is a function that takes another function as an argument or returns another function as a result. More specifically, a first-order function takes and returns base types, such as integers or lists. A kth-order function takes or returns a function of order k−1. Currying often artificially inflates the order of a function, so we will ignore all inessential currying. (Whether a particular instance of currying is essential or inessential is open to debate, but we expect that our choices will be uncontroversial.) In addition, when calculating the order of a polymorphic function, we instantiate all type variables with base types. Under these assumptions, most common higher-order functions, such as map and foldr, are second-order, so beginning functional programmers often wonder: What good are functions of order three or above? We illustrate functions of up to sixth-order with examples taken from a combinator parsing library.

Combinator parsing is a classic application of functional programming, dating back to at least Burge (1975). Most combinator parsers are based on Wadler's list-of-successes technique (Wadler, 1985). Hutton (1992) popularized the idea in his excellent tutorial Higher-Order Functions for Parsing. In spite of the title, however, he considered only functions of up to order three.

Cuneiform is a minimal functional programming language for large-scale scientific data analysis. Implementing a strict black-box view on external operators and data, it allows the direct embedding of code in a variety of external languages like Python or R, provides data-parallel higher order operators for processing large partitioned data sets, allows conditionals and general recursion, and has a naturally parallelizable evaluation strategy suitable for multi-core servers and distributed execution environments like Hadoop, HTCondor, or distributed Erlang. Cuneiform has been applied in several data-intensive research areas including remote sensing, machine learning, and bioinformatics, all of which critically depend on the flexible assembly of pre-existing tools and libraries written in different languages into complex pipelines. This paper introduces the computation semantics for Cuneiform. It presents Cuneiform's abstract syntax, a simple type system, and the semantics of evaluation. Providing an unambiguous specification of the behavior of Cuneiform eases the implementation of interpreters which we showcase by providing a concise reference implementation in Erlang. The similarity of Cuneiform's syntax to the simply typed lambda calculus puts Cuneiform in perspective and allows a straightforward discussion of its design in the context of functional programming. Moreover, the simple type system allows the deduction of the language's safety up to black-box operators. Last, the formulation of the semantics also permits the verification of compilers to and from other workflow languages.