Ranking Templates for Linear Loops
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Abstract (arXiv)
We present a new method for the constraint-based synthesis of termination arguments for linear loop programs based on linear ranking templates. Linear ranking templates are parametrized, well-founded relations such that an assignment to the parameters gives rise to a ranking function. This approach generalizes existing methods and enables us to use templates for many different ranking functions with affine-linear components. We discuss templates for multiphase, piecewise, and lexicographic ranking functions. Because these ranking templates require both strict and non-strict inequalities, we use Motzkin's Transposition Theorem instead of Farkas Lemma to transform the generated existsforall-constraint into an exists-constraint.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jan 2014.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1.
2 papers.
- 2021Neural Termination Analysiscited by 16
- 2024Neural Model Checkingcited by 6
- 2024Neural Model Checking31 Oct 2024
- 2021Neural Termination Analysis7 Feb 2021