Block Alpha-Circulant Preconditioners for All-at-Once Diffusion-Based Covariance Operators

TL;DR

This paper develops practical block alpha-circulant preconditioners for all-at-once diffusion-based covariance operators, improving computational efficiency and robustness when diagonalization is not straightforward.

Contribution

It introduces two novel methods using Chebyshev semi-iteration and saddle point reformulation to approximate block alpha-circulant preconditioners in complex diffusion systems.

Findings

Both methods match iteration counts of the best-case preconditioner with unlimited resources.

The nested Chebyshev approach enhances performance under limited computational budgets.

The approaches are robust and efficient in terms of outer iterations and matrix-vector products.

Abstract

Covariance matrices are central to data assimilation and inverse methods derived from statistical estimation theory. Previous work has considered the application of an all-at-once diffusion-based representation of a covariance matrix operator in order to exploit inherent parallelism in the underlying problem. In this paper, we provide practical methods to apply block $\alpha$-circulant preconditioners to the all-at-once system for the case where the main diffusion operation matrix cannot be readily diagonalized using a discrete Fourier transform. Our new framework applies the block $\alpha$-circulant preconditioner approximately by solving an inner block diagonal problem via a choice of inner iterative approaches. Our first method applies Chebyshev semi-iteration to a symmetric positive definite matrix, shifted by a complex scaling of the identity. We extend theoretical results for Chebyshev semi-iteration in the symmetric positive definite setting, to obtain computable bounds on the asymptotic convergence factor for each of the complex sub-problems. The second approach transforms the complex sub-problem into a (generalized) saddle point system with real coefficients. Numerical experiments reveal that in the case of unlimited computational resources, both methods can match the iteration counts of the `best-case' block $\alpha$-circulant preconditioner. We also provide a practical adaptation to the nested Chebyshev approach, which improves performance in the case of a limited computational budget. Using an appropriate choice of $\alpha$ our new approaches are robust and efficient in terms of outer iterations and matrix--vector products.