Xin Xing

Xin Xing, Edmond Chow

In the iterative solution of dense linear systems from boundary integral equations or systems involving kernel matrices, the main challenges are the expensive matrix-vector multiplication and the storage cost which are usually tackled by hierarchical matrix techniques such as $\mathcal{H}$ and $\mathcal{H}^2$ matrices. However, hierarchical matrices also have a high construction cost that is dominated by the low-rank approximations of the sub-blocks of the kernel matrix. In this paper, an efficient method is proposed to give a low-rank approximation of the kernel matrix block $K(X_0, Y_0)$ in the form of an interpolative decomposition (ID) for a kernel function $K(x,y)$ and two properly located point sets $X_0, Y_0$. The proposed method combines the ID using strong rank-revealing QR (sRRQR), which is purely algebraic, with analytic kernel information to reduce the construction cost of a rank-$r$ approximation from $O(r|X_0||Y_0|)$, for ID using sRRQR alone, to $O(r|X_0|)$ which is not related to $|Y_0|$. Numerical experiments show that $\mathcal{H}^2$ matrix construction with the proposed algorithm only requires a computational cost linear in the matrix dimension.

Ping Ma, Xinlian Zhang, Xin Xing et al.

The statistical analysis of Randomized Numerical Linear Algebra (RandNLA) algorithms within the past few years has mostly focused on their performance as point estimators. However, this is insufficient for conducting statistical inference, e.g., constructing confidence intervals and hypothesis testing, since the distribution of the estimator is lacking. In this article, we develop an asymptotic analysis to derive the distribution of RandNLA sampling estimators for the least-squares problem. In particular, we derive the asymptotic distribution of a general sampling estimator with arbitrary sampling probabilities. The analysis is conducted in two complementary settings, i.e., when the objective of interest is to approximate the full sample estimator or is to infer the underlying ground truth model parameters. For each setting, we show that the sampling estimator is asymptotically normally distributed under mild regularity conditions. Moreover, the sampling estimator is asymptotically unbiased in both settings. Based on our asymptotic analysis, we use two criteria, the Asymptotic Mean Squared Error (AMSE) and the Expected Asymptotic Mean Squared Error (EAMSE), to identify optimal sampling probabilities. Several of these optimal sampling probability distributions are new to the literature, e.g., the root leverage sampling estimator and the predictor length sampling estimator. Our theoretical results clarify the role of leverage in the sampling process, and our empirical results demonstrate improvements over existing methods.