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arXiv 2102.04052math.OC
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关于概率函数水平集的凸性

On the Convexity of Level-sets of Probability Functions

Yassine Laguel, Wim van Ackooij, Jérôme Malick, Guilherme Ramalho

摘要:

在不确定性决策问题中,概率约束是表达决策安全性的重要工具。它们是根据决策向量对一组给定的随机不等式进行概率测度而得到的。即使原来的一组不等式是凸的,这个有利的属性不会立即转移到概率约束的可行集,特别是可能取决于所选择的安全水平。在本文中,我们提供的结果,保证凸性的可行集概率约束时,安全水平是大于一个可计算的阈值。本文的结果推广了已有的结果,并涵盖了决策向量属于巴拿赫空间的情形.我们方法的关键思想是揭示名义问题数据中的潜在凸性水平(例如,概率函数的简化)。我们提供了几个例子来说明我们的理论发展。

英文摘要:

In decision-making problems under uncertainty, probabilistic constraints are a valuable tool to express safety of decisions. They result from taking the probability measure of a given set of random inequalities depending on the decision vector. Even if the original set of inequalities is convex, this favourable property is not immediately transferred to the probabilistically constrained feasible set and may in particular depend on the chosen safety level. In this paper, we provide results guaranteeing the convexity of feasible sets to probabilistic constraints when the safety level is greater than a computable threshold. Our results extend all the existing ones and also cover the case where decision vectors belong to Banach spaces. The key idea in our approach is to reveal the level of underlying convexity in the nominal problem data (e.g., concavity of the probability function) by auxiliary transforming functions. We provide several examples illustrating our theoretical developments.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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