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A comparison of approaches for finding minimum identifying codes on graphs

机译:在图上找到最小识别码的方法比较

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In order to formulate mathematical conjectures likely to be true, a number of base cases must be determined. However, many combinatorial problems are NP-hard and the computational complexity makes this research approach difficult using a standard brute force approach on a typical computer. One sample problem explored is that of finding a minimum identifying code. To work around the computational issues, a variety of methods are explored and consist of a parallel computing approach using MATLAB, an adiabatic quantum optimization approach using a D-Wave quantum annealing processor, and lastly using satisfiability modulo theory (SMT) and corresponding SMT solvers. Each of these methods requires the problem to be formulated in a unique manner. In this paper, we address the challenges of computing solutions to this NP-hard problem with respect to each of these methods.
机译:为了表达可能是正确的数学猜想,必须确定许多基本情况。但是,许多组合问题都是NP难题,并且计算复杂性使这种研究方法在典型计算机上使用标准蛮力方法变得困难。探索的一个样本问题是找到最小的识别码。为了解决计算问题,探索了多种方法,包括使用MATLAB的并行计算方法,使用D-Wave量子退火处理器的绝热量子优化方法,最后使用可满足性模理论(SMT)和相应的SMT求解器。这些方法中的每一种都要求以独特的方式解决问题。在本文中,我们针对每种方法解决了针对NP难题的计算解决方案的挑战。

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