A posteriori error estimates based on multilevel decompositions with large problems on the coarsest level

TL;DR

This paper introduces a new approach for residual-based a posteriori error estimates in multilevel methods, especially when the coarsest level system remains large, using conjugate gradient approximations to improve efficiency and robustness.

Contribution

It proposes a novel approximation technique for the coarsest level error term using conjugate gradient, enhancing estimate accuracy and robustness for large problems.

Findings

The new approximation improves the efficiency of error estimates.

The method maintains robustness regardless of coarsest problem size.

Numerical results confirm theoretical advantages.

Abstract

Multilevel methods represent a powerful approach in numerical solution of partial differential equations. The multilevel structure can also be used to construct estimates for total and algebraic errors of computed approximations. This paper deals with residual-based error estimates that are based on properties of quasi-interpolation operators, stable-splittings, or frames. We focus on the settings where the system matrix on the coarsest level is still large and the associated terms in the estimates can only be approximated. We show that the way in which the error term associated with the coarsest level is approximated is substantial. It can significantly affect both the efficiency (accuracy) of the overall error estimates and their robustness with respect to the size of the coarsest problem. The newly proposed approximation of the coarsest-level term is based on using the conjugate gradient method with an appropriate stopping criterion. We prove that the resulting estimates are efficient and robust with respect to the size of the coarsest-level problem. Numerical experiments illustrate the theoretical findings.