Linear Algebraic Analogues of the Graph Isomorphism Problem and the Erdős-Rényi Model

Yinan Li, Youming Qiao
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引用次数: 29

Abstract

A classical difficult isomorphism testing problem is to test isomorphism of p-groups of class 2 and exponent p in time polynomial in the group order. It is known that this problem can be reduced to solving the alternating matrix space isometry problem over a finite field in time polynomial in the underlying vector space size. We propose a venue of attack for the latter problem by viewing it as a linear algebraic analogue of the graph isomorphism problem. This viewpointleads us to explore the possibility of transferring techniques for graph isomorphism to this long-believed bottleneck case of group isomorphism.In 1970s, Babai, Erdős, and Selkow presented the first average-case efficient graph isomorphism testing algorithm (SIAM J Computing, 1980). Inspired by that algorithm, we devise an average-case efficient algorithm for the alternating matrix space isometry problem over a key range of parameters, in a random model of alternating matrix spaces in vein of the Erd∝os-R´enyi model of random graphs. For this, we develop a linear algebraic analogue of the classical individualisation technique, a technique belonging to a set of combinatorial techniques that has been critical for the progress on the worst-case time complexity for graph isomorphism, but was missing in the group isomorphism context. This algorithm also enables us to improve Higmans 57-year-old lower bound on the number of p-groups (Proc. of the LMS, 1960). We finally show that Luks dynamic programming technique for graph isomorphism (STOC 1999) can be adapted to slightly improve the worst-case time complexity of the alternating matrix space isometry problem in a certain range of parameters.Most notable progress on the worst-case time complexity of graph isomorphism, including Babais recent breakthrough (STOC 2016) and Babai and Luks previous record (STOC 1983), has relied on both group theoretic and combinatorial techniques. By developing a linear algebraic analogue of the individualisation technique and demonstrating its usefulness in the average-case setting, the main result opens up the possibility of adapting that strategy for graph isomorphism to this hard instance of group isomorphism. The linear algebraic Erdős-Rényi model is of independent interest and may deserve further study.
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图同构问题的线性代数类比及Erdős-Rényi模型
一个经典的同构检验难题是在群阶的时间多项式上检验2类p群和指数p群的同构。已知该问题可简化为求解有限域上的交替矩阵空间等距问题,在时间多项式下的基本向量空间大小。我们将后一个问题视为图同构问题的线性代数模拟,提出了一个攻击地点。这种观点引导我们探索将图同构技术转移到长期以来被认为是群同构瓶颈的情况下的可能性。在20世纪70年代,Babai, Erdős和Selkow提出了第一个平均情况下有效的图同构测试算法(SIAM J Computing, 1980)。受该算法的启发,我们在随机图的Erd∝os-R´enyi模型的随机交替矩阵空间模型中,设计了一种针对关键参数范围内的交替矩阵空间等距问题的平均情况有效算法。为此,我们开发了经典个体化技术的线性代数模拟,这种技术属于一组组合技术,对图同构的最坏情况时间复杂度的研究至关重要,但在群同构环境中却缺失了。该算法还使我们能够改进Higmans关于p群数量的57岁下界(Proc. of the LMS, 1960)。我们最后证明了Luks的图同构动态规划技术(STOC 1999)可以在一定参数范围内略微提高交替矩阵空间等距问题的最坏情况时间复杂度。在图同构的最坏情况时间复杂度方面最显著的进展,包括Babai和Luks最近的突破(STOC 2016)和Babai和Luks之前的记录(STOC 1983),都依赖于群论和组合技术。通过发展个体化技术的线性代数模拟并证明其在平均情况下的有用性,主要结果开辟了将图同构策略适应于群同构的这种困难实例的可能性。线性代数Erdős-Rényi模型具有独立的研究价值,值得进一步研究。
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