While, In General, Uncertainty Quantification (UQ) Is NP-Hard, Many Practical UQ Problems Can Be Made Feasible

Authors

Anderson Gray, University of LiverpoolFollow
Scott Ferson, University of LiverpoolFollow
Olga Kosheleva, The University of Texas at El PasoFollow
Vladik Kreinovich, The University of Texas at El PasoFollow

Publication Date

10-1-2021

Comments

Technical Report: UTEP-CS-21-74a

Published in Proceedings of the IEEE Series of Symposia on Computational Intelligence SSCI'2021, Orlando, Florida, December 4-7, 2021.

Abstract

In general, many general mathematical formulations of uncertainty quantification problems are NP-hard, meaning that (unless it turned out that P = NP) no feasible algorithm is possible that would always solve these problems. In this paper, we argue that if we restrict ourselves to practical problems, then the correspondingly restricted problems become feasible -- namely, they can be solved by using linear programming techniques.