Patrik Daniel: Monday 18 March at 14:00, A315 Inria Paris.

We propose new practical adaptive refinement algorithms for conforming hp-finite element approximations of elliptic problems. We consider the use of both exact and inexact solvers within the established framework of adaptive methods consisting of four concatenated modules: SOLVE, ESTIMATE, MARK, REFINE. The strategies are driven by guaranteed equilibrated flux a posteriori error estimators. Namely, for an inexact approximation obtained by an (arbitrary) iterative algebraic solver, the bounds for the total, the algebraic, and the discretization errors are provided. The nested hierarchy of hp-finite element spaces is crucially exploited for the algebraic error upper bound which in turn allows us to formulate sharp stopping criteria for the algebraic solver. Our hp-refinement criterion hinges on from solving two local residual problems posed on patches of elements around marked vertices selected by a bulk-chasing criterion. They respectively emulate h-refinement and p-refinement. One particular feature of our approach is that we derive a computable real number which gives a guaranteed bound on the ratio of the (unknown) energy error in the next adaptive loop step with respect to the present one (i.e. on the error reduction factor). Numerical experiments are presented to validate the proposed adaptive strategies. We investigate the accuracy of our bound on the error reduction factor which turns out to be excellent, with effectivity indices close to the optimal value of one. In practice, we observe asymptotic exponential convergence rates, in both the exact and inexact algebraic solver settings. Finally, we also provide a theoretical analysis of the proposed strategies. We prove that under some additional assumptions on the h- and p-refinements, including the interior node property and sufficient p-refinements, the computable reduction factors are indeed bounded by a generic constant strictly smaller than one. This implies the convergence of the adaptive strategies.