一般来说，不确定性量化(UQ)是np困难的，但许多实际的UQ问题是可行的

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI:10.1109/SSCI50451.2021.9659990

Ander Gray, S. Ferson, O. Kosheleva, V. Kreinovich

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引用次数: 1

摘要

一般来说，许多不确定性量化问题的一般数学公式是NP困难的，这意味着(除非P = NP)没有可行的算法可能总是解决这些问题。在本文中，我们认为，如果我们将自己限制在实际问题中，那么相应的限制问题就变得可行-即，它们可以通过使用线性规划技术来解决。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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, then the correspondingly restricted problems become feasible - namely, they can be solved by using linear programming techniques.

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2021 IEEE Symposium Series on Computational Intelligence (SSCI)

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