Optimal sampling for linear function approximation and high-order finite difference methods over complex regions I focus on algorithms that generate good sampling points for function approximation. In 1D, it is well known that polynomial interpolation using equispaced points is unstable. On the other hand, using Chebyshev nodes provides both stable and highly accurate points for polynomial interpolation. In higher dimensional complex regions, optimal sampling points are not known explicitly. This work presents robust algorithms that find good sampling points in complex regions for polynomial interpolation, least-squares, and radial basis function (RBF) methods. The quality of these nodes is measured using the Lebesgue constant. I will also consider optimal sampling for constrained optimization, used to solve PDEs, where boundary conditions must be imposed. Furthermore, I extend the scope of the problem to include finding near-optimal sampling points for high-order finite difference methods. These high-order finite difference methods can be implemented using either piecewise polynomials or RBFs.autLiu, TonythsPlatte, Rodrigo BdgcRenaut, RosemarydgcKaspar, DaviddgcMoustaoui, MohameddgcMotsch, SebastienpblArizona State UniversityengPartial requirement for: Ph.D., Arizona State University, 2019Includes bibliographical references (pages 86-89)Field of study: Mathematicsby Tony Liuhttps://hdl.handle.net/2286/R.I.5489700Doctoral DissertationAcademic thesesviii, 89 pages : illustrations (some color)115730763481630032421157649adminIn Copyright2019TextApplied MathematicsFinite Difference MethodsFunction ApproximationLebesgue constantOptimal SamplingSampling (Statistics)Linear models (Statistics)Approximation TheoryFinite differencesPolynomialsInterpolationFunctions of real variables