A posteriori stopping criteria for optimized Schwarz domain decomposition algorithms in mixed formulations

Abstract : This paper develops a posteriori estimates for domain decomposition methods with optimized Robin transmission conditions on the interface between subdomains. We choose to demonstrate the methodology for mixed formulations, with a lowest-order Raviart–Thomas–Nédélec discretization, often used for heterogeneous and anisotropic porous media diffusion problems. Our estimators allow to distinguish the spatial discretization and the domain decomposition error components. We propose an adaptive domain decomposition algorithm wherein the iterations are stopped when the domain decomposition error does not affect significantly the overall error. Two main goals are thus achieved. First, a guaranteed bound on the overall error is obtained at each step of the domain decomposition algorithm. Second, important savings in terms of the number of domain decomposition iterations can be realized. Numerical experiments illustrate the efficiency of our estimates and the performance of the adaptive stopping criteria.
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Sarah Ali Hassan, Caroline Japhet, Michel Kern, Martin Vohralík. A posteriori stopping criteria for optimized Schwarz domain decomposition algorithms in mixed formulations. Computational Methods in Applied Mathematics, De Gruyter, 2018, 18 (3), pp.495-519. ⟨https://www.degruyter.com/view/j/cmam.2018.18.issue-3/cmam-2018-0010/cmam-2018-0010.xml⟩. ⟨10.1515/cmam-2018-0010⟩. ⟨hal-01529532v3⟩

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