English(EN) Accelerating Floating-Point Satisfiability Solving via Gradient Normalization

新的GradSAT框架加速浮点可满足性求解

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-08 04:00

研究人员开发了GradSAT，一个增强可满足性模理论（SMT）求解器的新框架，特别针对无量词浮点（QF_FP）理论。该方法使用多任务学习将每个SMT子句视为独立任务，采用动态梯度归一化（GradNorm）来平衡梯度幅度，防止困难子句阻碍整体收敛。该系统具有用于连续松弛的GPU加速PyTorch后端和用于精确赋值解析的位精确局部搜索引擎，旨在提高约束求解的鲁棒性和并行性。 AI

影响该框架可以提高软件验证和程序分析工具的效率和鲁棒性。

排序理由该集群包含一篇研究论文，详细介绍了一个用于加速特定类型求解器的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Yuanzhuo Zhang · 2026-10-08 04:00

通过梯度归一化加速浮点可满足性求解

arXiv:2610.08808v1 Announce Type: cross Abstract: Satisfiability Modulo Theories (SMT) solvers are foundational to software verification, program analysis, and compiler testing, particularly over the theory of Quantifier-Free Floating-Point (QF_FP). While recent optimization-base…

本文如何被排名

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通过梯度归一化加速浮点可满足性求解

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