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Optimal Spectral-Norm Approximate Minimization of Weighted Finite Automata

Authors Borja Balle, Clara Lacroce , Prakash Panangaden , Doina Precup, Guillaume Rabusseau



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Author Details

Borja Balle
  • DeepMind, London, UK
Clara Lacroce
  • School of Computer Science, McGill University, Montréal, Canada
  • Mila, Montréal, Canada
Prakash Panangaden
  • School of Computer Science, McGill University, Montréal, Canada
  • Mila, Montréal, Canada
Doina Precup
  • School of Computer Science, McGill University, Montréal, Canada
  • Mila, Montréal, Canada
Guillaume Rabusseau
  • DIRO, Université de Montréal, Montréal, Canada
  • CIFAR AI Chair, Mila, Montréal, Canada

Acknowledgements

The authors would like to thank Tianyu Li, Harsh Satija and Alessandro Sordoni for feedback on earlier drafts of this work, Gheorghe Comanici for a detailed review, and Maxime Wabartha for fruitful discussions and comments on proofs.

Cite AsGet BibTex

Borja Balle, Clara Lacroce, Prakash Panangaden, Doina Precup, and Guillaume Rabusseau. Optimal Spectral-Norm Approximate Minimization of Weighted Finite Automata. In 48th International Colloquium on Automata, Languages, and Programming (ICALP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 198, pp. 118:1-118:20, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.ICALP.2021.118

Abstract

We address the approximate minimization problem for weighted finite automata (WFAs) with weights in ℝ, over a one-letter alphabet: to compute the best possible approximation of a WFA given a bound on the number of states. This work is grounded in Adamyan-Arov-Krein approximation theory, a remarkable collection of results on the approximation of Hankel operators. In addition to its intrinsic mathematical relevance, this theory has proven to be very effective for model reduction. We adapt these results to the framework of weighted automata over a one-letter alphabet. We provide theoretical guarantees and bounds on the quality of the approximation in the spectral and 𝓁² norm. We develop an algorithm that, based on the properties of Hankel operators, returns the optimal approximation in the spectral norm.

Subject Classification

ACM Subject Classification
  • Theory of computation → Quantitative automata
  • Theory of computation → Probabilistic computation
  • Theory of computation → Markov decision processes
Keywords
  • Weighted finite automata
  • approximate minimization
  • Hankel matrices
  • AAK Theory

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