Abstract In recent years, various means of efficiently detecting changepoints have been proposed, with one popular approach involving minimizing a penalized cost function using dynamic programming. In some situations, these algorithms can have an expected computational cost that is linear in the number of data points; however, the worst case cost remains quadratic. We introduce two means of improving the computational performance of these methods, both based on parallelizing the dynamic programming approach. We establish that parallelization can give substantial computational improvements: in some situations the computational cost decreases roughly quadratically in the number of cores used. These parallel implementations are no longer guaranteed to find the true minimum of the penalized cost; however, we show that they retain the same asymptotic guarantees in terms of their accuracy in estimating the number and location of the changes. Supplementary materials for this article are available online.

中文翻译：

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通用变化点检测方法的并行化

摘要 近年来，已经提出了各种有效检测变化点的方法，其中一种流行的方法是使用动态规划来最小化惩罚成本函数。在某些情况下，这些算法可能具有与数据点数量成线性关系的预期计算成本；然而，最坏情况下的成本仍然是二次的。我们介绍了两种提高这些方法计算性能的方法，这两种方法都基于并行化动态规划方法。我们确定并行化可以提供显着的计算改进：在某些情况下，计算成本随使用的内核数量大致呈二次方下降。这些并行实现不再保证找到惩罚成本的真正最小值；然而，我们表明，它们在估计变化的数量和位置的准确性方面保留了相同的渐近保证。本文的补充材料可在线获取。