Worst case tractability of linear problems in the presence of noise: linear information
Numerical Analysis
2023-03-30 v1 Numerical Analysis
Abstract
We study the worst case tractability of multivariate linear problems defined on separable Hilbert spaces. Information about a problem instance consists of noisy evaluations of arbitrary bounded linear functionals, where the noise is either deterministic or random. The cost of a single evaluation depends on its precision and is controlled by a cost function. We establish mutual interactions between tractability of a problem with noisy information, the cost function, and tractability of the same problem, but with exact information.
Keywords
Cite
@article{arxiv.2303.16328,
title = {Worst case tractability of linear problems in the presence of noise: linear information},
author = {Leszek Plaskota and Paweł Siedlecki},
journal= {arXiv preprint arXiv:2303.16328},
year = {2023}
}