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A generalized complexity measure based on Rényi entropy

机译:基于Rényi熵的广义复杂度测度

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摘要

The intrinsic statistical complexities of finite many-particle systems (i.e., those defined in terms of the single-particle density) quantify the degree of structure or patterns, far beyond the entropy measures. They are intuitively constructed to be minima at the opposite extremes of perfect order and maximal randomness. Starting from the pioneering LMC measure, which satisfies these requirements, some extensions of LMC-Rényi type have been published in the literature. The latter measures were shown to describe a variety of physical aspects of the internal disorder in atomic and molecular systems (e.g., quantum phase transitions, atomic shell filling) which are not grasped by their mother LMC quantity. However, they are not minimal for maximal randomness in general. In this communication, we propose a generalized LMC-Rényi complexity which overcomes this problem. Some applications which illustrate this fact are given.
机译:有限的多粒子系统的固有统计复杂性(即以单粒子密度定义的那些)量化结构或模式的程度,远远超出了熵的度量范围。它们直观地构造为在完美顺序和最大随机性的相反极端处为最小值。从满足这些要求的开创性的LMC度量开始,文献中已经发布了LMC-Rényi类型的一些扩展。已显示后一种措施描述了原子和分子系统内部无序的各种物理方面(例如,量子相变,原子壳填充),而其母LMC量却无法掌握这些方面。但是,对于最大随机性而言,它们并不是最小的。在此通信中,我们提出了克服此问题的广义LMC-Rényi复杂度。给出了一些说明这一事实的应用。

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