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Graphic Type Constraints and Efficient Type Inference : from ML to MLF

Boris Yakobowski and Didier Remy

The 13th ACM SIGPLAN International Conference on Functional Programming (ICFP 2008)
Victoria, British Columbia, Canada, September 22-24, 2008


Abstract

MLF is a type system that seamlessly merges ML-style type inference with System-F polymorphism. We propose a system of graphic (type) constraints that can be used to perform type inference in both ML or MLF. We show that this constraint system is a small extension of the formalism of graphic types, originally introduced to represent MLF types. We give a few semantic preserving transformations on constraints and propose a strategy for applying them to solve constraints. We show that the resulting algorithm has optimal complexity for MLF type inference, and argue that, as for ML, this complexity is linear under reasonable assumptions.


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