Cost-optimal adaptive FEM with linearization and algebraic solver for semilinear elliptic PDEs

TL;DR

This paper presents an adaptive finite element method for semilinear elliptic PDEs with strongly monotone nonlinearities, employing linearization and algebraic solvers to ensure optimal convergence and computational efficiency.

Contribution

It introduces a fully adaptive algorithm combining mesh refinement, linearization, and algebraic solving, with proven optimal complexity for semilinear elliptic PDEs.

Findings

Proves convergence of the adaptive method with optimal complexity.

Demonstrates effectiveness of the approach through numerical experiments.

Analyzes the influence of adaptivity parameters on performance.

Abstract

We consider scalar semilinear elliptic PDEs, where the nonlinearity is strongly monotone, but only locally Lipschitz continuous. To linearize the arising discrete nonlinear problem, we employ a damped Zarantonello iteration, which leads to a linear Poisson-type equation that is symmetric and positive definite. The resulting system is solved by a contractive algebraic solver such as a multigrid method with local smoothing. We formulate a fully adaptive algorithm that equibalances the various error components coming from mesh refinement, iterative linearization, and algebraic solver. We prove that the proposed adaptive iteratively linearized finite element method (AILFEM) guarantees convergence with optimal complexity, where the rates are understood with respect to the overall computational cost (i.e., the computational time). Numerical experiments investigate the involved adaptivity parameters.