Continuous Optimization for Satisfiability Modulo Theories on Linear Real Arithmetic

📰 ArXiv cs.AI

FourierSMT is a new continuous-variable optimization framework for satisfiability modulo theories (SMT) that is scalable and highly parallelizable

advanced Published 25 Mar 2026

Action Steps

Understand the limitations of existing conflict-driven clause learning approaches to SMT
Recognize the potential of continuous-variable optimization for improving scalability and parallelization
Apply FourierSMT to industrial applications such as hardware verification and design automation

Who Needs to Know This

This benefits software engineers and AI researchers working on formal verification and design automation, as it improves the efficiency of SMT solutions

Key Insight

💡 Continuous-variable optimization can be used to improve the efficiency and scalability of SMT solutions

Key Takeaways

FourierSMT is a new continuous-variable optimization framework for satisfiability modulo theories (SMT) that is scalable and highly parallelizable

Full Article

Title: Continuous Optimization for Satisfiability Modulo Theories on Linear Real Arithmetic

Abstract:
arXiv:2603.22877v1 Announce Type: new Abstract: 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 generaliz

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