<div class="csl-bib-body">
<div class="csl-entry">Bringmann, P. (2023). How to prove optimal convergence rates for adaptive least-squares finite element methods. <i>Journal of Numerical Mathematics</i>, <i>31</i>(1), 43–58. https://doi.org/10.1515/jnma-2021-0116</div>
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dc.identifier.issn
1570-2820
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/191979
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dc.description.abstract
The convergence analysis with rates for adaptive least-squares finite element methods (ALSFEMs) combines arguments from the a posteriori analysis of conforming and mixed finite element schemes. This paper provides an overview of the key arguments for the verification of the axioms of adaptivity for an ALSFEM for the solution of a linear model problem. The formulation at hand allows for the simultaneous analysis of first-order systems of the Poisson model problem, the Stokes equations, and the linear elasticity equations. Following [Carstensen and Park, SIAM J. Numer. Anal. 53(1), 2015], the adaptive algorithm is driven by an alternative residual-based error estimator with exact solve and includes a separate marking strategy for quasi-optimal data resolution of the right-hand side. This presentation covers conforming discretisations for an arbitrary polynomial degree and mixed homogeneous boundary conditions.
en
dc.language.iso
en
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dc.publisher
WALTER DE GRUYTER GMBH
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dc.relation.ispartof
Journal of Numerical Mathematics
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dc.subject
adaptive mesh-refinement
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dc.subject
alternative a posteriori error estimator
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dc.subject
higher-order discretisations
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dc.subject
least-squares finite element method
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dc.subject
linear elasticity equations
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dc.subject
mixed boundary conditions
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dc.subject
optimal convergence rates
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dc.subject
Poisson model problem
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dc.subject
separate marking
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dc.subject
Stokes equations
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dc.title
How to prove optimal convergence rates for adaptive least-squares finite element methods