A Monadic, Functional Implementation of Real Numbers

Large scale real number computation is an essential ingredient in several modern mathematical proofs. Because such lengthy computations cannot be verified by hand, some mathematicians want to use software proof assistants to verify the correctness of these proofs. This paper develops a new implementation of the constructive real numbers and elementary functions for such proofs by using the monad properties of the completion operation on metric spaces. Bishop and Bridges's notion of regular sequences is generalized to, what I call, regular functions which form the completion of any metric space. Using the monad operations, continuous functions on length spaces (a common subclass of metric spaces) are created by lifting continuous functions on the original space. A prototype Haskell implementation has been created. I believe that this approach yields a real number library that is reasonably efficient for computation, and still simple enough to easily verify its correctness.Comment: This paper is to appear in an upcoming issue of Mathematical Structures in Computer Science published by Cambridge University Press. For more information and the latest source code for Few Digits, see <http://r6.ca/FewDigits/

Memoizing a monadic mixin DSL

Modular extensibility is a highly desirable property of a domain-specific language (DSL): the ability to add new features without affecting the implementation of existing features. Functional mixins (also known as open recursion) are very suitable for this purpose. We study the use of mixins in Haskell for a modular DSL for search heuristics used in systematic solvers for combinatorial problems, that generate optimized C++ code from a high-level specification. We show how to apply memoization techniques to tackle performance issues and code explosion due to the high recursion inherent to the semantics of combinatorial search. As such heuristics are conventionally implemented as highly entangled imperative algorithms, our Haskell mixins are monadic. Memoization of monadic components causes further complications for us to deal with

Gradual Certified Programming in Coq

Expressive static typing disciplines are a powerful way to achieve high-quality software. However, the adoption cost of such techniques should not be under-estimated. Just like gradual typing allows for a smooth transition from dynamically-typed to statically-typed programs, it seems desirable to support a gradual path to certified programming. We explore gradual certified programming in Coq, providing the possibility to postpone the proofs of selected properties, and to check "at runtime" whether the properties actually hold. Casts can be integrated with the implicit coercion mechanism of Coq to support implicit cast insertion a la gradual typing. Additionally, when extracting Coq functions to mainstream languages, our encoding of casts supports lifting assumed properties into runtime checks. Much to our surprise, it is not necessary to extend Coq in any way to support gradual certified programming. A simple mix of type classes and axioms makes it possible to bring gradual certified programming to Coq in a straightforward manner.Comment: DLS'15 final version, Proceedings of the ACM Dynamic Languages Symposium (DLS 2015

Tracing monadic computations and representing effects

In functional programming, monads are supposed to encapsulate computations, effectfully producing the final result, but keeping to themselves the means of acquiring it. For various reasons, we sometimes want to reveal the internals of a computation. To make that possible, in this paper we introduce monad transformers that add the ability to automatically accumulate observations about the course of execution as an effect. We discover that if we treat the resulting trace as the actual result of the computation, we can find new functionality in existing monads, notably when working with non-terminating computations.Comment: In Proceedings MSFP 2012, arXiv:1202.240

Generating reversible circuits from higher-order functional programs

Boolean reversible circuits are boolean circuits made of reversible elementary gates. Despite their constrained form, they can simulate any boolean function. The synthesis and validation of a reversible circuit simulating a given function is a difficult problem. In 1973, Bennett proposed to generate reversible circuits from traces of execution of Turing machines. In this paper, we propose a novel presentation of this approach, adapted to higher-order programs. Starting with a PCF-like language, we use a monadic representation of the trace of execution to turn a regular boolean program into a circuit-generating code. We show that a circuit traced out of a program computes the same boolean function as the original program. This technique has been successfully applied to generate large oracles with the quantum programming language Quipper.Comment: 21 pages. A shorter preprint has been accepted for publication in the Proceedings of Reversible Computation 2016. The final publication is available at http://link.springer.co

How functional programming mattered

In 1989 when functional programming was still considered a niche topic, Hughes wrote a visionary paper arguing convincingly ‘why functional programming matters’. More than two decades have passed. Has functional programming really mattered? Our answer is a resounding ‘Yes!’. Functional programming is now at the forefront of a new generation of programming technologies, and enjoying increasing popularity and influence. In this paper, we review the impact of functional programming, focusing on how it has changed the way we may construct programs, the way we may verify programs, and fundamentally the way we may think about programs

Type Generic Observing

Observing intermediate values helps to understand what is going on when your program runs. Gill presented an observation method for lazy functional languages that preserves the program's semantics. However, users need to define for each type how its values are observed: a laborious task and strictness of the program can easily be affected. Here we define how any value can be observed based on the structure of its type by applying generic programming frameworks. Furthermore we present an extension to specify per observation point how much to observe of a value. We discuss especially functional values and behaviour based on class membership in generic programming frameworks