Plain convergence of adaptive algorithms without exploiting reliability and efficiency

Abstract

We consider h-adaptive algorithms in the context of the finite element method (FEM) and the boundary element method (BEM). Under quite general assumptions on the building blocks SOLVE, ESTIMATE, MARK, and REFINE of such algorithms, we prove plain convergence in the sense that the adaptive algorithm drives the underlying a posteriori error estimator to zero. Unlike available results in the literature, our analysis avoids the use of any reliability and efficiency estimate, but only relies on structural properties of the estimator, namely stability on non-refined elements and reduction on refined elements. In particular, the new framework thus covers also problems involving non-local operators like the fractional Laplacian or boundary integral equations, where (discrete) efficiency is (currently) not available.

References in corpus (2)

• Further results on a space-time FOSLS formulation of parabolic PDEs
• A short note on plain convergence of adaptive least-squares finite element methods

Cited by in corpus (4)

• Adaptive Computation of Elliptic Eigenvalue Topology Optimization with a Phase-Field Approach
• Plain convergence of goal-oriented adaptive FEM
• Adaptive Approximations of Inclusions in a Semilinear Elliptic Problem Related to Cardiac Electrophysiology
• Convergence Analysis of an Adaptive Nonconforming FEM for Phase-Field Dependent Topology Optimization in Stokes Flow