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• W2551482414 abstract "We consider the problem of automatically and efficiently computing models of constraints, in the presence of complex background theories such as floating-point arithmetic. Constructing models, or proving that a constraint is unsatisfiable, has various applications, for instance for automatic generation of test inputs. It is well-known that a naïve encoding of constraints into simpler theories (for instance, bit-vectors or propositional logic) often leads to a drastic increase in size, or that it is unsatisfactory in terms of the resulting space and runtime demands. We define a framework for systematic application of approximations in order to improve performance. Our method is more general than previous techniques in the sense that approximations that are neither under- nor over-approximations can be used, and it shows promising performance on practically relevant benchmark problems." @default.
• W2551482414 created "2016-11-30" @default.
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• W2551482414 date "2016-11-10" @default.
• W2551482414 modified "2023-09-27" @default.
• W2551482414 title "An Approximation Framework for Solvers and Decision Procedures" @default.
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• W2551482414 doi "https://doi.org/10.1007/s10817-016-9393-1" @default.
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