论文标题
块离散经验插值方法
Block Discrete Empirical Interpolation Methods
论文作者
论文摘要
我们提出了离散经验插值法（Deim）的块变体；作为一种特定的应用程序，我们将考虑一个cur分解。块DEIM算法基于最大体积的概念和远程vealing QR分解。我们还提供了块Deim过程的版本，该版本允许自适应块大小。实验的结果表明，与标准DEIM程序相比，块DEIM算法对于低级矩阵近似值表现出可比的精度。但是，DEIM算法也表现出潜在的计算优势，从而显示了计算时间的提高效率。
We present block variants of the discrete empirical interpolation method (DEIM); as a particular application, we will consider a CUR factorization. The block DEIM algorithms are based on the concept of the maximum volume of submatrices and a rank-revealing QR factorization. We also present a version of the block DEIM procedures, which allows for adaptive choice of block size. The results of the experiments indicate that the block DEIM algorithms exhibit comparable accuracy for low-rank matrix approximation compared to the standard DEIM procedure. However, the block DEIM algorithms also demonstrate potential computational advantages, showcasing increased efficiency in terms of computational time.
