Continuous Optimization for Satisfiability Modulo Theories on Linear Real Arithmetic
Efficient solutions for satisfiability modulo theories (SMT) are integral in industrial applications such as hardware verification and design automation. Existing approaches are predominantly based on conflict-driven clause learning, which is structurally difficult to parallelize and therefore scales poorly. In this work, we introduce FourierSMT as a scalable and highly parallelizable continuous-variable optimization framework for SMT. We generalize the Walsh-Fourier expansion (WFE), called extended WFE (xWFE), from the Boolean domain to a mixed Boolean-real domain, which allows the use of gradient methods for SMT. This addresses the challenge of finding satisfying variable assignments to...
| Authors | Yunuo Cen, Daniel Ebler, Xuanyao Fong |
|---|---|
| Primary category | cs.AI |
| Published | 24 March 2026 |
| Last updated at source | 24 March 2026 |
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