Degree of Best Approximation by Trigonometric Blending Functions.
Density in approximation theory.
Density methods and results in approximation theory
Approximation theory and functional analysis share many common problems and points of contact. One of the areas of mutual interest is that of density results. In this paper we briefly survey various methods and results in this area starting from work of Weierstrass and Riesz, and extending to more recent times.
Density of Polynomials in the L^2 Space on the Real and the Imaginary Axes and in a Sobolev Space
2000 Mathematics Subject Classification: 41A10, 30E10, 41A65.In this paper we consider an L^2 type space of scalar functions L^2 M, A (R u iR) which can be, in particular, the usual L^2 space of scalar functions on R u iR. We find conditions for density of polynomials in this space using a connection with the L^2 space of square-integrable matrix-valued functions on R with respect to a non-negative Hermitian matrix measure. The completness of L^2 M, A (R u iR ) is also established.
Derivatives of integrating functions for orthonormal polynomials with exponential-type weights.
Die Eindeutigkeit L2-optimaler Polynomialer Monosplines.
Die Güte der Lp-Approximation durch Kantorovic-Polynome.
Dini-Campanato spaces and applications to nonlinear elliptic equations.
Discrete, Linear Approximation Problems in Polyhedral Norms.
Discrete linear interpolatory operators.
Distinguishing and integrating aleatoric and epistemic variation in uncertainty quantification
Much of uncertainty quantification to date has focused on determining the effect of variables modeled probabilistically, and with a known distribution, on some physical or engineering system. We develop methods to obtain information on the system when the distributions of some variables are known exactly, others are known only approximately, and perhaps others are not modeled as random variables at all.The main tool used is the duality between risk-sensitive integrals and relative entropy, and we...