TY - JOUR T1 - Optimal Order Convergence Implies Numerical Smoothness. AU - SO–HSIANG CHOU JO - International Journal of Numerical Analysis Modeling Series B VL - 4 SP - 357 EP - 373 PY - 2014 DA - 2014/05 SN - 5 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/ijnamb/240.html KW - Adaptive algorithm KW - discontinuous Galerkin KW - numerical smoothness KW - optimal order convergence AB - It is natural to expect the following loosely stated approximation principle to hold: a numerical approximation solution should be in some sense as smooth as its target exact solution in order to have optimal convergence. For piecewise polynomials, that means we have to at least maintain numerical smoothness in the interiors as well as across the interfaces of cells or elements. In this paper we give clear definitions of numerical smoothness that address the across-interface smoothness in terms of scaled jumps in derivatives [9] and the interior numerical smoothness in terms of differences in derivative values. Furthermore, we prove rigorously that the principle can be simply stated as numerical smoothness is necessary for optimal order convergence. It is valid on quasi-uniform meshes by triangles and quadrilaterals in two dimensions and by tetrahedrons and hexahedrons in three dimensions. With this validation we can justify, among other things, incorporation of this principle in creating adaptive numerical approximation for the solution of PDEs or ODEs, especially in designing proper smoothness indicators or detecting potential nonconvergence and instability.

SO–HSIANG CHOU. (2014). Optimal Order Convergence Implies Numerical Smoothness.. International Journal of Numerical Analysis Modeling Series B. 5 (4). 357-373. doi: