Unconditional full linear convergence and optimal complexity of adaptive iteratively linearized FEM for nonlinear PDEs

We propose an adaptive iteratively linearized finite element method (AILFEM) in the context of strongly monotone nonlinear operators with energy structure in Hilbert spaces. The approach combines adaptive mesh-refinement with an energy-contractive linearization scheme (e.g., the Kačanov method) and a norm-contractive algebraic solver (e.g., an optimal geometric multigrid method). Crucially, a novel parameter-free algebraic stopping criterion is designed and we prove that it leads to a uniformly bounded number of algebraic solver steps. Unlike available results requiring sufficiently small adaptivity parameters to ensure even plain convergence, the new AILFEM algorithm guarantees full R-linear convergence for arbitrary adaptivity parameters. Thus, unconditional convergence is guaranteed. Moreover, for sufficiently small adaptive mesh-refinement parameter θ θ and linearization-stopping parameter λ l i n λ ₗᵢₙ , the new adaptive algorithm guarantees optimal complexity, i.e., optimal convergence rates with respect to the overall computational cost and hence time.