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SIAMNUM
2010

On the Strong Convergence of Gradients in Stabilized Degenerate Convex Minimization Problems

13 years 7 months ago
On the Strong Convergence of Gradients in Stabilized Degenerate Convex Minimization Problems
Infimizing sequences in nonconvex variational problems typically exhibit enforced finer and finer oscillations called microstructures such that the infimal energy is not attained. Although those oscillations are physically meaningful, finite element approximations experience difficulty in their reconstruction. The relaxation of the nonconvex minimization problem by (semi) convexification leads to a macroscopic model for the effective energy. The resulting discrete macroscopic problem is degenerate in the sense that it is convex but not strictly convex. This paper studies a modified discretization by adding a stabilization term to the discrete energy. It will be proven that for a wide class of problems, this stabilization technique leads to strong H1 convergence of the macroscopic variables even on unstructured triangulations. In contrast to the work [C. Carstensen, P. Plech
Wolfgang Boiger, Carsten Carstensen
Added 21 May 2011
Updated 21 May 2011
Type Journal
Year 2010
Where SIAMNUM
Authors Wolfgang Boiger, Carsten Carstensen
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