On sampling discretization in L_2

09/22/2020
by   Irina Limonova, et al.
0

A sampling discretization theorem for the square norm of functions from a finite dimensional subspace satisfying Nikol'skii's inequality is proved. The obtained upper bound on the number of sampling points is of the order of the dimension of the subspace.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/25/2023

Sampling discretization of the uniform norm and applications

Discretization of the uniform norm of functions from a given finite dime...
research
04/28/2021

On exact discretization of the L_2-norm with a negative weight

For a subspace X of functions from L_2 we consider the minimal number m ...
research
09/29/2021

Discretizing L_p norms and frame theory

Given an N-dimensional subspace X of L_p([0,1]), we consider the problem...
research
11/09/2020

Discretization on high-dimensional domains

Let μ be a Borel probability measure on a compact path-connected metric ...
research
04/02/2021

A remark on discretization of the uniform norm

Discretization of the uniform norm of functions from a given finite dime...
research
07/23/2021

Universal sampling discretization

Let X_N be an N-dimensional subspace of L_2 functions on a probability s...
research
09/24/2020

L_2-norm sampling discretization and recovery of functions from RKHS with finite trace

We provide a spectral norm concentration inequality for infinite random ...

Please sign up or login with your details

Forgot password? Click here to reset