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arXiv 2102.00855cs.CC
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采样与配分函数的复杂性

Sampling and Complexity of Partition Function

Chuyu Xiong

摘要:

数划分问题是一个著名的问题,它是21个Karp的NP完全问题之一,称为\cite{karp}。该划分函数是一个布尔函数,它等价于有限值域的数划分问题。充分理解数划分问题和划分函数的计算复杂性是一个十分重要而又困难的问题。人们推测,我们需要新的工具和方法\cite{aaronson}这样的问题。在我们最近对通用学习机\cite{paper5, paper8}的研究中,我们开发了一些工具,即拟合极值,适当的采样集,带参数的布尔函数(以试错的方式使用)。我们发现这些工具可以应用于配分函数。在这篇文章中,我们讨论了配分函数的设置,配分函数的性质,以及要使用的工具。这种方法使我们证明了划分函数的计算复杂性的下界,以及数划分问题的计算复杂性的下界,是指数问题的大小。这意味着:P $\ne$ NP \cite{cook}。

英文摘要:

The number partition problem is a well-known problem, which is one of 21 Karp's NP-complete problems \cite{karp}. The partition function is a boolean function that is equivalent to the number partition problem with number range restricted. To fully understand the computational complexity of the number partition problem and the partition function is quite important and hard. People speculate that we need new tools and methods \cite{aaronson} for such problem. In our recent research on the universal learning machine \cite{paper5, paper8}, we developed some tools, namely, fitting extremum, proper sampling set, boolean function with parameters (used in trial-and-error fashion). We found that these tools could be applied to the partition function. In this article, we discuss the set up of the partition function, properties of the partition function, and the tools to be used. This approach leads us to prove that the lower bound of the computational complexity of partition function, as well as the lower bound of the computational complexity of the number partition problem, is exponential to the size of problem. This implies: P $\ne$ NP \cite{cook}.

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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