Interpolating unstructured data using barycentric coordinates becomes infeasible at high dimensions due to the prohibitive memory requirements of building a Delaunay triangulation. We present a new algorithm to construct ad-hoc simplices that are empirically guaranteed to contain the target coordinates, based on a nearest neighbor heuristic and an iterative dimensionality reduction through projection. We use these simplices to interpolate the astrophysical cooling function Λ and show that this new approach produces good results with just a fraction of the previously required memory.

中文翻译：

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PSI：构建临时单纯形以插入高维非结构化数据

由于构建 Delaunay 三角剖分的内存要求过高，使用重心坐标对非结构化数据进行插值在高维上变得不可行。我们提出了一种基于最近邻启发式和通过投影的迭代降维的新算法来构造从经验上保证包含目标坐标的 ad-hoc 单纯形。我们使用这些单纯形对天体物理冷却函数 Λ 进行插值，并表明这种新方法只需以前所需内存的一小部分即可产生良好的结果。