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arXiv 2407.19781cs.SC
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实量词消去与柱面代数分解的最新进展

Recent Developments in Real Quantifier Elimination and Cylindrical Algebraic Decomposition

Matthew England

AI总结:

本文综述了实量词消去与柱面代数分解的最新进展,重点探讨了受计算逻辑启发的算法改进、SAT/SMT协同应用、机器学习优化变量排序及可解释AI的潜在贡献。

摘要:

本扩展摘要伴随CASC 2024的受邀报告,综述了实量词消去(QE)与柱面代数分解(CAD)的最新进展。在介绍这些概念后,我们首先考虑受计算逻辑启发的CAD改进,特别是支撑现代SAT求解器的算法。CAD理论已通过可满足性模理论(SMT)范式与这些求解器协同应用;同时SAT/SMT背后的思想催生了实量词消去的新算法。其次,我们考虑通过机器学习(ML)优化CAD。CAD变量排序的选择已成为在计算机代数中使用ML调整算法的关键案例。我们还将探讨可解释AI技术如何在不依赖ML的最终代码下,为改进计算机代数软件提供洞见。

英文摘要:

This extended abstract accompanies an invited talk at CASC 2024, which surveys recent developments in Real Quantifier Elimination (QE) and Cylindrical Algebraic Decomposition (CAD). After introducing these concepts we will first consider adaptations of CAD inspired by computational logic, in particular the algorithms which underpin modern SAT solvers. CAD theory has found use in collaboration with these via the Satisfiability Modulo Theory (SMT) paradigm; while the ideas behind SAT/SMT have led to new algorithms for Real QE. Second we will consider the optimisation of CAD through the use of Machine Learning (ML). The choice of CAD variable ordering has become a key case study for the use of ML to tune algorithms in computer algebra. We will also consider how explainable AI techniques might give insight for improved computer algebra software without any reliance on ML in the final code.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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