While, In General, Uncertainty Quantification (UQ) Is NP-Hard, Many Practical UQ Problems Can Be Made Feasible
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...
Saved in:
Published in | 2021 IEEE Symposium Series on Computational Intelligence (SSCI) pp. 01 - 06 |
---|---|
Main Authors | , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
05.12.2021
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | 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. |
---|---|
DOI: | 10.1109/SSCI50451.2021.9659990 |