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FreezeML is a new approach to first-class polymorphic type inference that employs term annotations to control when and how polymorphic types are instantiated and generalised. It conservatively extends Hindley-Milner type inference and was first presented as an extension to Algorithm W. More modern type inference techniques such as HM(X) and OutsideIn(X) employ constraints to support features such as type classes, type families, rows, and other extensions. We take the first step towards modernising FreezeML by presenting a constraint-based type inference algorithm. We introduce a new constraint language, inspired by the Pottier/Rémy presentation of HM(X), in order to allow FreezeML type inference problems to be expressed as constraints. We present a deterministic stack machine for solving FreezeML constraints and prove its termination and correctness.
|Journal||Proceedings of the ACM on Programming Languages|
|Publication status||Published - 31 Aug 2022|
|Event||The 27th ACM SIGPLAN International Conference on Functional Programming, 2022 - Ljubljana, Slovenia|
Duration: 11 Sep 2022 → 16 Sep 2022
Conference number: 27
- first-class polymorphism
- type inference
- impredicative types
- 1 Conference contribution
Emrich, F., Lindley, S., Stolarek, J., Cheney, J. & Coates, J., 11 Jun 2020, Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation. Association for Computing Machinery (ACM), p. 423-437 15 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference contributionOpen AccessFile