Automating Sized Type Inference for Complexity Analysis (Technical Report)
Logic in Computer Science
2017-06-29 v1 Computational Complexity
Abstract
This paper introduces a new methodology for the complexity analysis of higher-order functional programs, which is based on three ingredients: a powerful type system for size analysis and a sound type inference procedure for it, a ticking monadic transformation, and constraint solving. Noticeably, the presented methodology can be fully automated, and is able to analyse a series of examples which cannot be handled by most competitor methodologies. This is possible due to the choice of adopting an abstract index language and index polymorphism at higher ranks. A prototype implementation is available.
Cite
@article{arxiv.1706.09169,
title = {Automating Sized Type Inference for Complexity Analysis (Technical Report)},
author = {Martin Avanzini and Ugo Dal Lago},
journal= {arXiv preprint arXiv:1706.09169},
year = {2017}
}
Comments
Technical report of http://dx.doi.org/10.1145/3110287