Enhancing multigrid solvers for isogeometric analysis of nonlinear problems using polynomial extrapolation techniques

TL;DR

This paper enhances multigrid solvers for nonlinear isogeometric analysis problems by integrating polynomial extrapolation techniques, achieving quadratic convergence and improved efficiency in solving complex nonlinear equations.

Contribution

It introduces a new theoretical framework linking residual and error norms and demonstrates the effectiveness of polynomial extrapolation in accelerating nonlinear solvers.

Findings

Polynomial extrapolation accelerates convergence of nonlinear solvers.

Quadratic convergence results for extrapolation methods are established.

Numerical experiments confirm improved efficiency in solving nonlinear problems.

Abstract

When used to accelerate the convergence of fixed-point iterative methods, such as the Picard method, which is a kind of nonlinear fixed-point iteration, polynomial extrapolation techniques can be very effective. The numerical solution of nonlinear problems is further investigated in this study. Particularly, using multigrid with isogeometric analysis as a linear solver of the Picard iterative method, which is accelerated by applying vector extrapolation techniques, is how we address the nonlinear eigenvalue Bratu problem and the Monge-Amp\`ere equation. This paper provides quadratic convergence results for polynomial extrapolation methods. Specifically, a new theoretical result on the correlation between the residual norm and the error norm, as well as a new estimation for the generalized residual norm of some extrapolation methods, are given. We perform an investigation between the Picard method, the Picard method accelerated by polynomial extrapolation techniques, and the Anderson accelerated Picard method. Several numerical experiments show that the Picard method accelerated by polynomial extrapolation techniques can solve these nonlinear problems efficiently.