计算多边形轨迹之间的Skorokhod距离

R. Majumdar, Vinayak S. Prabhu
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引用次数: 21

摘要

Skorokhod距离是连续和混合系统轨迹的自然度量。它测量两个迹线之间的最佳匹配,每个迹线将时间间隔[0,T]映射到度量空间O,当允许连续双客观时序失真时。形式上,它计算所有时序失真中两个分量的最大值的最小值:第一个分量量化时序失真的时序差异,第二个分量量化时序失真后值的不匹配(在度量空间O中)。Skorokhod距离出现在各种基本的混合系统分析问题中:从混合系统语义和等效概念的定义,到实际问题,如检查模型的紧密性或模拟的质量。尽管它在语义上得到了广泛的应用,但两个有限采样时间混合轨迹之间的Skorokhod距离的计算问题仍然没有解决。我们解决了计算两个多边形轨迹之间的Skorokhod距离的问题(当采样时间轨迹通过采样点之间的线性插值完成时,这些轨迹就会出现)。我们提供了一种算法,当使用n维的L1, L2和L∞范数比较跟踪值时,可以计算精确的Skorokhod距离。我们的算法基于对fr切距离的简化,是完全多项式时间的,并且在三个规范上为IRn中的一组几何原语合并了新的多项式时间过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computing the Skorokhod distance between polygonal traces
The Skorokhod distance is a natural metric on traces of continuous and hybrid systems. It measures the best match between two traces, each mapping a time interval [0, T] to a metric space O, when continuous bijective timing distortions are allowed. Formally, it computes the infimum, over all timing distortions, of the maximum of two components: the first component quantifies the timing discrepancy of the timing distortion, and the second quantifies the mismatch (in the metric space O) of the values after the timing distortion. Skorokhod distances appear in various fundamental hybrid systems analysis concerns: from definitions of hybrid systems semantics and notions of equivalence, to practical problems such as checking the closeness of models or the quality of simulations. Despite its extensive use in semantics, the computation problem for the Skorokhod distance between two finite sampled-time hybrid traces remained open. We address the problem of computing the Skorokhod distance between two polygonal traces (these traces arise when sampled-time traces are completed by linear interpolation between sample points). We provide an algorithm to compute the exact Skorokhod distance when trace values are compared using the L1, L2, and L∞ norms in n dimensions. Our algorithm, based on a reduction to Fréchet distances, is fully polynomial-time, and incorporates novel polynomial-time procedures for a set of geometric primitives in IRn over the three norms.
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