@InProceedings{Remy-Yakobowski@icfp08:mlf-type-inference,
AUTHOR = {Didier R{\'e}my and Boris Yakobowski},
TITLE  = {{E}fficient {T}ype {I}nference for the {MLF} Language:
          a graphical and constraints-based approach},
BOOKTITLE = {The 13th ACM SIGPLAN 
             International Conference on Functional Programming (ICFP'08)}, 
YEAR = 2008,
ADDRESS = {Victoria, BC, Canada},
MONTH = Sep,
ALSO  =  {http://gallium.inria.fr/~remy/mlf/},
PDF =    {http://gallium.inria.fr/~remy/mlf/Remy-Yakobowski@icfp08:mlf-type-inference.pdf},
pages = {63--74},
doi = {http://doi.acm.org/10.1145/1411203.1411216},
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.
}
}