学术文献库

In the computational sciences, one must often estimate model parameters from data subject to noise and uncertainty, leading to inaccurate results. In order to improve the accuracy of models with noisy parameters, we consider the problem of reducing error in an elliptic linear system with the operator corrupted by noise. We assume the noise preserves positive definiteness, but otherwise, we make no additional assumptions the structure of the noise. Under these assumptions, we propose the operator shifting framework, a collection of easy-to-implement algorithms that augment a noisy inverse operator by subtracting an additional auxiliary term. In a similar fashion to the James-Stein estimator, this has the effect of drawing the noisy inverse operator closer to the ground truth, and hence reduces error by reducing both bias and variance. We develop bootstrap Monte Carlo algorithms to estimate the required augmentation magnitude for optimal error reduction in the noisy system. To improve the tractability of these algorithms, we propose several approximate polynomial expansions for the operator inverse, and prove desirable convergence and monotonicity properties for these expansions. We also prove theorems that quantify the error reduction obtained by operator augmentation. In addition to theoretical results, we provide a set of numerical experiments on four different graph and grid Laplacian systems that all demonstrate effectiveness of our method.