← 研踪 PaperTrailarXiv:2611.00162 · v2
Beyond Typical Strings: Sampling the Complexity Landscape
首次提交 2026-10-30 18:31
版本更新 2026-11-09 23:59
雷达观测 2026-11-10 15:22
摘要
Uniform random sampling of strings is overwhelmingly dominated by high-complexity strings, leaving the much smaller set of low- and intermediate-complexity strings largely unexplored. Here, we adapt the nested neutral set estimator (NNSE), a Monte Carlo rare-event sampling method, to estimate the distribution P(K) of complexity values for binary strings of fixed length. NNSE allows us to explore the full complexity landscape, including exponentially rare low-complexity regions that are inaccessible to direct sampling, for string lengths up to several hundred. We apply the method to several widely used complexity measures: Lempel-Ziv 76 (LZ76), Lempel-Ziv 78 (LZ78), context tree weighting (CTW), the block decomposition method (BDM), and block entropy. We further introduce uniform complexity weighting (UCW), which gives equal weight to different complexity levels rather than to individual strings. Using NNSE to perform UCW sampling allows us to compare complexity measures across their full range in a dataset-independent way. This reveals differences in their behavior for low- and intermediate-complexity strings that are practically inaccessible to conventional uniform sampling.
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v2 · 2026-11-09 23:59 · 最新观测版本
Beyond Typical Strings: Sampling the Complexity Landscape
Uniform random sampling of strings is overwhelmingly dominated by high-complexity strings, leaving the much smaller set of low- and intermediate-complexity strings largely unexplored. Here, we adapt the nested neutral set estimator (NNSE), a Monte Carlo rare-event sampling method, to estimate the distribution P(K) of complexity values for binary strings of fixed length. NNSE allows us to explore the full complexity landscape, including exponentially rare low-complexity regions that are inaccessible to direct sampling, for string lengths up to several hundred. We apply the method to several widely used complexity measures: Lempel-Ziv 76 (LZ76), Lempel-Ziv 78 (LZ78), context tree weighting (CTW), the block decomposition method (BDM), and block entropy. We further introduce uniform complexity weighting (UCW), which gives equal weight to different complexity levels rather than to individual strings. Using NNSE to perform UCW sampling allows us to compare complexity measures across their full range in a dataset-independent way. This reveals differences in their behavior for low- and intermediate-complexity strings that are practically inaccessible to conventional uniform sampling.
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