Abstract
Automated reasoning procedures are essential for a number of applications that involve bit-exact floating-point computations. This paper presents conditions that characterize when a variable in a floating-point constraint has a solution, which we call invertibility conditions. We describe a novel workflow that combines human interaction and a syntax-guided synthesis (SyGuS) solver that was used for discovering these conditions. We verify our conditions for several floating-point formats. One implication of this result is that a fragment of floating-point arithmetic admits compact quantifier elimination. We implement our invertibility conditions in a prototype extension of our solver CVC4, showing their usefulness for solving quantified constraints over floating-points.
Cite
CITATION STYLE
Brain, M., Niemetz, A., Preiner, M., Reynolds, A., Barrett, C., & Tinelli, C. (2019). Invertibility conditions for floating-point formulas. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11562 LNCS, pp. 116–136). Springer Verlag. https://doi.org/10.1007/978-3-030-25543-5_8
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