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...

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Bibliographic Details
Published in2021 IEEE Symposium Series on Computational Intelligence (SSCI) pp. 01 - 06
Main Authors Gray, Ander, Ferson, Scott, Kosheleva, Olga, Kreinovich, Vladik
Format Conference Proceeding
LanguageEnglish
Published IEEE 05.12.2021
Subjects
Computational intelligence
decision making
Linear programming
NP-hard
p-box
Programming
Uncertainty
uncertainty quantification
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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