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arXiv 2207.02571cs.DS
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利用MAX-SAT计算NP难重复性测度

Computing NP-hard Repetitiveness Measures via MAX-SAT

Hideo Bannai, Keisuke Goto, Masakazu Ishihata, Shunsuke Kanda, Dominik Köppl, Takaaki Nishimoto

摘要:

重复性度量揭示了数据集的深刻特征,并产生了在压缩空间中工作的压缩数据结构和算法。可惜的是,其中一些度量的计算是NP难的,并且直接计算对于即使是小尺寸的数据集也是不可行的。三个这样的措施是最小尺寸的字符串吸引子,最小尺寸的双向宏计划,和最小尺寸的直线程序。虽然存在大量的各种各样的实现方法来计算近似值,但这些度量的精确计算几乎没有受到关注。在本文中,我们提出了MAX-SAT配方,提供了第一个非平凡的实现精确计算最小的字符串吸引子,最小的双向宏计划,最小的直线程序。计算实验表明,我们的实现工作的文本长度高达几百直线程序和双向宏计划,文本甚至超过一百万字符串吸引。

英文摘要:

Repetitiveness measures reveal profound characteristics of datasets, and give rise to compressed data structures and algorithms working in compressed space. Alas, the computation of some of these measures is NP-hard, and straight-forward computation is infeasible for datasets of even small sizes. Three such measures are the smallest size of a string attractor, the smallest size of a bidirectional macro scheme, and the smallest size of a straight-line program. While a vast variety of implementations for heuristically computing approximations exist, exact computation of these measures has received little to no attention. In this paper, we present MAX-SAT formulations that provide the first non-trivial implementations for exact computation of smallest string attractors, smallest bidirectional macro schemes, and smallest straight-line programs. Computational experiments show that our implementations work for texts of length up to a few hundred for straight-line programs and bidirectional macro schemes, and texts even over a million for string attractors.

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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