Leveraging linear and mixed integer programming for SMT

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Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks (2017, background): “Another direction is to provide better soundness guarantees without harming performance, for example by replaying floating-point solutions using precise arithmetic [17], or by producing externally-checkable correctness proofs [15].”
Effective Algorithms for the Satisfiability of Quantifier-Free Formulas Over Linear Real and Integer Arithmetic (2014, background): “The content of this chapter is to appear in FMCAD’14 [74].”
cvc5: A Versatile and Industrial-Strength SMT Solver (2022, methodology): “It implements a sum-of-infeasibilities-based heuristic [79], an integration with the external GLPK LP solver [80], and certain heuristics proposed by Griggio [63].”