We prove new Bernstein and Markov type inequalities in spaces associated with the normal and the tangential derivatives on the boundary of a general compact -domain with . These estimates are also applied to establish Marcinkiewicz type inequalities for discretization of norms of algebraic polynomials on -domains with asymptotically optimal number of function samples used.
We prove lower bounds on the error incurred when approximating any oscillating function using piecewise polynomial spaces. The estimates are explicit in the polynomial degree and have optimal dependence on the meshwidth and frequency when the polynomial degree is fixed. These lower bounds, for example, apply when approximating solutions to Helmholtz plane wave scattering problem.
We consider the task of recovering a Sobolev function on a connected compact Riemannian manifold when given a sample on a finite point set. We prove that the quality of the sample is given by the -average of the geodesic distance to the point set and determine the value of . This extends our findings on bounded convex domains [IMA J. Numer. Anal., 2024]. As a byproduct, we prove the optimal rate of convergence of the th minimal worst case error for -approximation for all .
Further, a limit theorem for moments of the average distance to a set consisting of i.i.d. uniform points is proven. This yields that a random sample is asymptotically as good as an optimal sample in precisely those cases with . In particular, we obtain that cubature formulas with random nodes are asymptotically as good as optimal cubature formulas if the weights are chosen correctly. This closes a logarithmic gap left open by Ehler, Gräf and Oates [Stat. Comput., 29:1203-1214, 2019].
Let be the class of algebraic polynomials of degree , all of whose zeros lie on the segment . In 1995, S. P. Zhou has proved the following Turán type reverse Markov–Nikol’skii inequality: , , where ,