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Counting Polycubes without the Dimensionality Curse

机译:计算没有多维诅咒的多维数据集

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摘要

A d-D polycube of size n is a connected set of n cells (hy-percubes) of an orthogonal d-dimensional lattice, where connectivity is through (d - 1)-dimensional faces of the cells. Computing A_d(n), the number of distinct d-dimensional polycubes of size n, is a long-standing elusive problem in discrete geometry. In a previous work we described the generalization from two to higher dimensions of a polyomino-counting algorithm of Redelmeier. The main deficiency of the algorithm is that it keeps the entire set of cells that appear in any possible polycube in memory at all times. Thus, the amount of required memory grows exponentially with the dimension. In this paper we present a method whose order of memory consumption is a (very low) polynomial in both n and d. Furthermore, we parallelized the algorithm and ran it through the Internet on dozens of computers simultaneously. This enables us to find A_d(n) for values of d and n far beyond any previous attempt.
机译:大小为n的d-D多维数据集是正交d维网格的n个单元(hy-percubes)的连接集合,其中连通性是通过单元的(d-1)维表面进行的。计算A_d(n)(大小为n的不同d维多立方体)的数量是离散几何中一个长期存在的难以解决的问题。在先前的工作中,我们描述了Redelmeier的多氨基酸计数算法从二维到更高维度的概括。该算法的主要缺陷在于,它始终将出现在任何可能的多维数据集中的整个单元集保留在内存中。因此,所需的内存量随尺寸呈指数增长。在本文中,我们提出了一种方法,其内存消耗的顺序在n和d中都是(非常低的)多项式。此外,我们对算法进行了并行处理,并同时通过Internet在数十台计算机上运行了该算法。这使我们能够找到d和n值的A_d(n),这远远超出了先前的任何尝试。

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