Quantifier Elimination Meets Treewidth

作者：Hao Wu, Jiyu Zhu, Amir Kafshdar Goharshady, Jie An, Bican Xia, Naijun Zhan · 发表于：Lecture notes in computer science · 年份：2026 · DOI：10.1007/978-3-032-22752-2_19 · 被引用次数：1 · 研究领域：Constraint Satisfaction and Optimization、Complexity and Algorithms in Graphs、Evolutionary Algorithms and Applications

In this paper, we address the complexity barrier inherent in Fourier-Motzkin elimination (FME) and cylindrical algebraic decomposition (CAD) when eliminating a block of (existential) quantifiers. To mitigate this, we propose exploiting structural sparsity in the variable dependency graph of quantified formulas. Utilizing tools from parameterized algorithms, we investigate the role of treewidth , a parameter that measures the graph’s tree-likeness, in the process of quantifier elimination. A novel dynamic programming framework, structured over a tree decomposition of the dependency graph, is developed for applying FME and CAD, and is also extensible to general quantifier elimination procedures. Crucially, we prove that when the treewidth is a constant, the framework achieves a significant exponential complexity improvement for both FME and CAD, reducing the worst-case complexity bound from doubly exponential to single exponential. Preliminary experiments on sparse linear real arithmetic (LRA) and nonlinear real arithmetic (NRA) benchmarks confirm that our algorithm outperforms the existing popular heuristic-based approaches on instances exhibiting low treewidth.