Discrete ABP Estimate and Convergence Rates for Linear Elliptic Equations in Non-divergence Form

• DOI: 10.1007/s10208-017-9347-y
• Publication Date: 2017-03-01T20:25:16Z
• Authors: Ricardo H. Nochetto; Wujun Zhang
• Keywords: Pointwise; Condition number

45 pages, 5 figures<br/>We design a two-scale finite element method (FEM) for linear elliptic PDEs in non-divergence form $A(x) : D^2 u(x) = f(x)$ in a bounded but not necessarily convex domain $��$ and study it in the max norm. The fine scale is given by the meshsize $h$ whereas the coarse scale $��$ is dictated by an integro-differential approximation of the PDE. We show that the FEM satisfies the discrete maximum principle (DMP) for any uniformly positive definite matrix $A$ provided that the mesh is face weakly acute. We establish a discrete Alexandroff-Bakelman-Pucci (ABP) estimate which is suitable for finite element analysis. Its proof relies on a discrete Alexandroff estimate which expresses the min of a convex piecewise linear function in terms of the measure of its sub-differential, and thus of jumps of its gradient. The discrete ABP estimate leads, under suitable regularity assumptions on $A$ and $u$, to pointwise error estimates of the form \begin{equation*} \| u - u^��_h \|_{L_\infty(��)} \leq \, C(A,u) \, h^{2��/(2 + ��)} \big| \ln h \big| \qquad 0< ��\leq 2, \end{equation*} provided $��\approx h^{2/(2+��)}$. Such a convergence rate is at best of order $ h \big| \ln h \big|$, which turns out to be quasi-optimal.<br/>