Abstract. Perhaps surprisingly, there are infinite sets that admit mechanical exhaustive search in finite time. We investigate three related questions: What kinds of infinite sets admit mechanical exhaustive search in finite time? How do we systematically build such sets? How fast can exhaustive search over infinite sets be performed? Keywords. Higher-type computability and complexity, Kleene–Kreisel functionals, PCF, Haskell, topology. 1.

Pure functional languages such as Haskell support programming with impure effects by exploiting mathematical notions such as monads, applicative functors, and arrows. However, in contrast to the wealth of research on the use of these notions to write effectful programs, there has been comparatively little progress on reasoning about the resulting programs. In this article we focus on this problem, using a simple but instructive example concerned with relabelling binary trees. 1

high-level and platform-independent description of digital signal processing (DSP) algorithms. Feldspar is a pure functional language embedded in Haskell. It offers a high-level dataflow style of programming, as well as a more mathematical style based on vector indices. The key to generating efficient code from such descriptions is a high-level optimization technique called vector fusion. Feldspar is based on a low-level, functional core language which has a relatively small semantic gap to machine-oriented languages like C. The core language serves as the interface to the back-end code generator, which produces C. For very small examples, the generated code performs comparably to hand-written C code when run on a DSP target. While initial results are promising, to achieve good performance on larger examples, issues related to memory access patterns and array copying will have to be addressed. I.

Functional programming languages ought to play a central role in mathematics education for middle schools (age range: 10–14). After all, functional programming is a form of algebra and programming is a creative activity about problem solving. Introducing it into mathematics courses would make pre-algebra course come alive. If input and output were invisible, students could implement fun simulations, animations, and even interactive and distributed games all while using nothing more than plain mathematics. We have implemented this vision with a simple framework for purely functional I/O. Using this framework, students design, implement, and test plain mathematical functions over numbers, booleans, string, and images. Then the framework wires them up to devices and performs all the translation from external information to internal data (and vice versa)—just like every other operating system. Once middle school students are hooked on this form of programming, our curriculum provides a smooth path for them from pre-algebra to freshman courses in college on object-oriented design and theorem proving. Categories and Subject Descriptors D.2.10 [Software Engineering]:

The Quantum IO monad is a purely functional interface to quantum programming implemented as a Haskell library. At the same time it provides a constructive semantics of quantum programming. The QIO monad separates reversible (i.e. unitary) and irreversible (i.e. probabilistic) computations and provides a reversible let operation (ulet), allowing us to use ancillas (auxiliary qubits) in a modular fashion. QIO programs can be simulated either by calculating a probability distribution or by embedding it into the IO monad using the random number generator. As an example we present a complete implementation of Shor’s algorithm.

Functional Reactive Programming (FRP) is an approach to reactive programming where systems are structured as networks of functions operating on time-varying values (signals). FRP is based on the synchronous data-flow paradigm and supports both continuous-time and discretetime signals (hybrid systems). What sets FRP apart from most other reactive languages is its support for systems with highly dynamic structure (dynamism) and higher-order reactive constructs (higher-order data-flow). However, the price paid for these features has been the loss of the safety and performance guarantees provided by other, less expressive, reactive languages. Statically guaranteeing safety properties of programs is an attractive proposition. This is true in particular for typical application domains for reactive programming such as embedded systems. To that end, many existing reactive languages have type systems or other static checksthatguaranteedomain-specificconstraints, suchasfeedbackbeingwell-formed(causality analysis). However, comparedwithFRP,theyarelimitedintheircapacitytosupportdynamism andhigher-orderdata-flow. Ontheotherhand, asestablishedstatictechniquesdonotsufficefor highly structurally dynamic systems, FRP generally enforces few domain-specific constraints, leaving the FRP programmer to manually check that the constraints are respected. Thus, there

The worker/wrapper transformation is a general technique for improving the performance of recursive programs by changing their types. The previous formalisation (Gill & Hutton, 2009) was based upon a simple fixed point semantics of recursion. In this article we develop a more structured approach, based upon initial algebra semantics. In particular, we show how the worker/wrapper transformation can be applied to programs defined using the structured pattern of recursion captured by fold operators, and illustrate our new technique with a number of examples.

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Monads as an organizing principle for programming and semantics are notoriously difficult to grasp, yet they are a central and powerful abstraction in Haskell. This paper introduces a domain-specific language, MonadLab, that simplifies the construction of monads, and describes its implementation in Template Haskell. MonadLab makes monad construction truly first class, meaning that arcane theoretical issues with respect to monad transformers are completely hidden from the programmer. The motivation behind the design of MonadLab is to make monadic programming in Haskell simpler while providing a tool for non-Haskell experts that will assist them in understanding this powerful abstraction.

The worker/wrapper transformation is a general technique for improving the performance of recursive programs by changing their types. The previous formalisation (Gill & Hutton, 2009) was based upon a simple fixed point semantics of recursion. In this article we develop a more structured approach, based upon initial algebra semantics. In particular, we show how the worker/wrapper transformation can be applied to programs defined using the structured pattern of recursion captured by fold operators, and illustrate our new technique with a number of examples.