Abstract
Policy iterations have been known in static analysis since a small decade. Despite the impressive results they provide - achieving a precise fixpoint without the need of widening/narrowing mechanisms of abstract interpretation - their use is not yet widespread. Furthermore, there are basically two dual approaches: min-policies and max-policies, but they have not yet been practically compared.
Multiple issues could explain their relative low adoption in the research communities: implementation of the theory is not obvious; initialization is rarely addressed; integration with other abstraction or fixpoint engine not mentionned; etc. This paper tries to present a Policy Iteration Primer, summarizing the approaches from the practical side, focusing on their implementation and use.
We implemented both of them for a specific setting: the computation of quadratic templates, which appear useful to analyze controllers such as found in civil aircrafts or UAVs.
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Roux, P., Garoche, PL. (2014). Computing Quadratic Invariants with Min- and Max-Policy Iterations: A Practical Comparison. In: Jones, C., Pihlajasaari, P., Sun, J. (eds) FM 2014: Formal Methods. FM 2014. Lecture Notes in Computer Science, vol 8442. Springer, Cham. https://doi.org/10.1007/978-3-319-06410-9_38
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DOI: https://doi.org/10.1007/978-3-319-06410-9_38
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