The power of random information for numerical approximation and integration
Numerical Analysis
2022-09-16 v1 Numerical Analysis
Probability
Abstract
This thesis investigates the quality of randomly collected data by employing a framework built on information-based complexity, a field related to the numerical analysis of abstract problems. The quality or power of gathered information is measured by its radius which is the uniform error obtainable by the best possible algorithm using it. The main aim is to present progress towards understanding the power of random information for approximation and integration problems.
Cite
@article{arxiv.2209.07266,
title = {The power of random information for numerical approximation and integration},
author = {Mathias Sonnleitner},
journal= {arXiv preprint arXiv:2209.07266},
year = {2022}
}
Comments
Phd thesis, University of Passau (2022), 165 pages. Based on arXiv:1907.06435, arXiv:2009.11275, arXiv:2010.04522, arXiv:2109.14504