Generalized Parikh Matrices For Tracking Subsequence Occurrences

Szilárd Zsolt Fazekas, Xinhao Huang
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Abstract

We introduce and study a generalized Parikh matrix mapping based on tracking the occurrence counts of special types of subsequences. These matrices retain more information about a word than the original Parikh matrix mapping while preserving the homomorphic property. We build the generalization by first introducing the Parikh factor matrix mapping and extend it to the Parikh sequence matrix mapping. We establish an interesting connection between the generalized Parikh matrices and the original ones and use it to prove that certain important minors of a Parikh sequence matrix have nonnegative determinant. Finally, we generalize the concept of subword histories and show that each generalized subword history is equivalent to a linear one.
用于跟踪后续出现的广义帕里克矩阵
我们介绍并研究了一种基于跟踪特殊类型子序列出现次数的广义 Parikh 矩阵映射。与原始的帕里克矩阵映射相比,这些矩阵保留了更多的单词信息,同时保留了同态属性。我们首先介绍了帕里克因子矩阵映射,然后将其扩展到帕里克序列矩阵映射,从而建立了这种泛化方法。我们在广义的帕里克矩阵和原始矩阵之间建立了有趣的联系,并用它证明了帕里克序列矩阵的某些重要最小值具有非负的决定性。最后,我们概括了子字历史的概念,并证明每个广义子字历史等价于线性子字历史。
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