Numerical Invariants through Convex Relaxation and Max-Strategy Iteration

Thomas Martin Gawlitza; Helmut Seidl

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arXiv · 1204.1147

Numerical Invariants through Convex Relaxation and Max-Strategy Iteration

Thomas Martin Gawlitza·Helmut Seidl

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Abstract

In this article we develop a max-strategy improvement algorithm for computing least fixpoints of operators on on the reals that are point-wise maxima of finitely many monotone and order-concave operators. Computing the uniquely determined least fixpoint of such operators is a problem that occurs frequently in the context of numerical program/systems verification/analysis. As an example for an application we discuss how our algorithm can be applied to compute numerical invariants of programs by abstract interpretation based on quadratic templates.

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Thomas Martin Gawlitza, Helmut Seidl. 2012-04-05. Numerical Invariants through Convex Relaxation and Max-Strategy Iteration. https://arxiv.org/abs/1204.1147

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