Optimization Modulo Theories (OMT) is an important extension of SMT which allows for finding models that optimize given objective functions, typically consisting in linear-arithmetic or pseudo-Boolean terms. However, many SMT and OMT applications, mostly from SW and HW verification, require handling bit-precise representations of numbers, which in SMT are handled by means of the theory of Bit-Vectors (BV) for the integers and that of Floating-Point Numbers (FP) for the reals respectively. Whereas an approach for OMT with (unsigned) BV has been proposed by Nadel & Ryvchin, unfortunately we are not aware of any existing approach for OMT with FP.

In this paper we fill this gap. We present a novel OMT approach, based on the novel concept of attractor and dynamic attractor, which extends the work of Nadel & Ryvchin to signed BV and, most importantly, to FP. We have implemented some OMT(BV) and OMT(FP) procedures on top of OptiMathSAT and tested the latter ones on modified problems from the SMT-LIB repository. The empirical results support the validity and feasibility of the novel approach.