The ksmt calculus is a δ-complete decision procedure for non-linear constraints
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Abstract (arXiv)
ksmt is a CDCL-style calculus for solving non-linear constraints over real numbers involving polynomials and transcendental functions. In this paper we investigate properties of the ksmt calculus and show that it is a δ-complete decision procedure for bounded problems. We also propose an extension with local linearisations, which allow for more efficient treatment of non-linear constraints.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Apr 2021.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1.
2 papers.
- 2021Bayesian Optimisation with Formal Guaranteescited by 0
- 2024SMLP: Symbolic Machine Learning Provercited by 0
- 2024SMLP: Symbolic Machine Learning Prover2 Feb 2024
- 2021Bayesian Optimisation with Formal Guarantees10 Jun 2021