Automated Sized-Type Inference and Complexity Analysis
Logic in Computer Science
2017-04-20 v1 Programming Languages
Abstract
This paper introduces a new methodology for the complexity analysis of higher-order functional programs, which is based on three components: a powerful type system for size analysis and a sound type inference procedure for it, a ticking monadic transformation and a concrete tool for 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 various key ingredients, and in particular an abstract index language and index polymorphism at higher ranks. A prototype implementation is available.
Keywords
Cite
@article{arxiv.1704.05585,
title = {Automated Sized-Type Inference and Complexity Analysis},
author = {Martin Avanzini and Ugo Dal Lago},
journal= {arXiv preprint arXiv:1704.05585},
year = {2017}
}
Comments
In Proceedings DICE-FOPARA 2017, arXiv:1704.05169