On the Versatility of Open Logical Relations: Continuity, Automatic Differentiation, and a Containment Theorem
Not on the map. Why?
Abstract (arXiv)
Logical relations are one of the most powerful techniques in the theory of programming languages, and have been used extensively for proving properties of a variety of higher-order calculi. However, there are properties that cannot be immediately proved by means of logical relations, for instance program continuity and differentiability in higher-order languages extended with real-valued functions. Informally, the problem stems from the fact that these properties are naturally expressed on terms of non-ground type (or, equivalently, on open terms of base type), and there is no apparent good definition for a base case (i.e. for closed terms of ground types). To overcome this issue, we study a generalization of the concept of a logical relation, called open logical relation, and prove that it can be fruitfully applied in several contexts in which the property of interest is about expressions of first-order type. Our setting is a simply-typed λ-calculus enriched with real numbers and real-valued first-order functions from a given set, such as the one of continuous or differentiable functions. We first prove a containment theorem stating that for any such a collection of functions including projection functions and closed under function composition, any well-typed term of first-order type denotes a function belonging to that collection. Then, we show by way of open logical relations the correctness of the core of a recently published algorithm for forward automatic differentiation. Finally, we define a refinement-based type system for local continuity in an extension of our calculus with conditionals, and prove the soundness of the type system using open logical relations.
Key facts. Cited by 5 papers in the map (data through 13 Oct 2026). First posted on arXiv 19 Feb 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 0; 2026: 2.
5 papers.
- 2022Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inferencecited by 6
- 2023On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameterscited by 3
- 2022Efficient and Sound Differentiable Programming in a Functional Array-Processing Languagecited by 0
- 2026GradInf: Gradient Estimation as Probabilistic Inferencecited by 0
- 2026LazyHMC: Hamiltonian Monte Carlo Simulation for Lazy, Infinite Dimensional Probabilistic Programscited by 0
- 2026LazyHMC: Hamiltonian Monte Carlo Simulation for Lazy, Infinite Dimensional Probabilistic Programs9 Aug 2026
- 2026GradInf: Gradient Estimation as Probabilistic Inference8 Jul 2026
- 2023On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters31 Jan 2023
- 2022Efficient and Sound Differentiable Programming in a Functional Array-Processing Language20 Dec 2022
- 2022Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference22 Aug 2022