论文标题
关于Chebyshev Norm中低级矩阵的最佳近似算法
On the algorithm of best approximation by low rank matrices in the Chebyshev norm
论文作者
论文摘要
低级别矩阵近似问题在计算数学中无处不在。传统上，此问题在光谱或Frobenius规范中解决，其中近似的准确性与矩阵奇异值的降低率有关。但是，最近的结果表明，对于其他规范，这一要求是不需要的。在本文中，我们提出了一种解决Chebyshev Norm中低级别近似问题的方法，该方法能够有效地构建矩阵的准确近似值，其奇异值不会缓慢降低或降低。
The low-rank matrix approximation problem is ubiquitous in computational mathematics. Traditionally, this problem is solved in spectral or Frobenius norms, where the accuracy of the approximation is related to the rate of decrease of the singular values of the matrix. However, recent results indicate that this requirement is not necessary for other norms. In this paper, we propose a method for solving the low-rank approximation problem in the Chebyshev norm, which is capable of efficiently constructing accurate approximations for matrices, whose singular values do not decrease or decrease slowly.
