Abstract
While abstract interpretation is not theoretically restricted to specific kinds of properties, it is, in practice, mainly developed to compute linear over-approximations of reachable sets, aka. the collecting semantics of the program. The verification of user-provided properties is not easily compatible with the usual forward fixpoint computation using numerical abstract domains.
We propose here to rely on sums-of-squares programming to characterize a property-driven polynomial invariant. This invariant generation can be guided by either boundedness, or in contrary, a given zone of the state space to avoid.
While the target property is not necessarily inductive with respect to the program semantics, our method identifies a stronger inductive polynomial invariant using numerical optimization. Our method applies to a wide set of programs: a main while loop composed of a disjunction (if-then-else) of polynomial updates e.g. piecewise polynomial controllers. It has been evaluated on various programs.
A. Adjé and P.-L. Garoche—is supported by the RTRA /STAE Project BRIEFCASE and the ANR ASTRID VORACE Project.
V. Magron—is supported by EPSRC (EP/I020457/1) Challenging Engineering Grant.
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- 1.
Note that most existing SDP solvers are implemented based on inexact computation. In practice, we perform post-processing verification (Yalmip command “checkset”), ensuring that computed polynomials are SOS.
- 2.
This approach also handles semialgebraic program termination.
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Adjé, A., Garoche, PL., Magron, V. (2015). Property-based Polynomial Invariant Generation Using Sums-of-Squares Optimization. In: Blazy, S., Jensen, T. (eds) Static Analysis. SAS 2015. Lecture Notes in Computer Science(), vol 9291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48288-9_14
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DOI: https://doi.org/10.1007/978-3-662-48288-9_14
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