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Typable Fragments of Polynomial Automatic Amortized Resource Analysis

Authors Long Pham , Jan Hoffmann

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ACM Subject Classification
  • Theory of computation → Type theory
Keywords
  • Resource consumption
  • Quantitative analysis
  • Amortized analysis
  • Typability

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Abstract

Being a fully automated technique for resource analysis, automatic amortized resource analysis (AARA) can fail in returning worst-case cost bounds of programs, fundamentally due to the undecidability of resource analysis. For programmers who are unfamiliar with the technical details of AARA, it is difficult to predict whether a program can be successfully analyzed in AARA. Motivated by this problem, this article identifies classes of programs that can be analyzed in type-based polynomial AARA. Firstly, it is shown that the set of functions that are typable in univariate polynomial AARA coincides with the complexity class PTime. Secondly, the article presents a sufficient condition for typability that axiomatically requires every sub-expression of a given program to be polynomial-time. It is proved that this condition implies typability in multivariate polynomial AARA under some syntactic restrictions.

Cite As Get BibTex

Long Pham and Jan Hoffmann. Typable Fragments of Polynomial Automatic Amortized Resource Analysis. In 29th EACSL Annual Conference on Computer Science Logic (CSL 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 183, pp. 34:1-34:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/LIPIcs.CSL.2021.34

Author Details

Long Pham
  • Carnegie Mellon University, Pittsburgh, PA, USA
Jan Hoffmann
  • Carnegie Mellon University, Pittsburgh, PA, USA

Funding

This article is based on research supported by DARPA under AA Contract FA8750-18-C-0092 and by the National Science Foundation under SaTC Award 1801369, CAREER Award 1845514, and SHF Awards 1812876 and 2007784. Any opinions, findings, and conclusions contained in this document are those of the authors and do not necessarily reflect the views of the sponsoring organizations. Long Pham gratefully acknowledges the support of the Funai Overseas Scholarship by the Funai Foundation of Information Technology, Japan.

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