A distributionally robust perspective on uncertainty quantification and chance constrained programming
نویسندگان
چکیده
منابع مشابه
A distributionally robust perspective on uncertainty quantification and chance constrained programming
The objective of uncertainty quantification is to certify that a given physical, engineering or economic system satisfies multiple safety conditions with high probability. A more ambitious goal is to actively influence the system so as to guarantee and maintain its safety, a scenario which can be modeled through a chance constrained program. In this paper we assume that the parameters of the sy...
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In this paper, we discuss linear programs in which the data that specify the constraints are subject to random uncertainty. A usual approach in this setting is to enforce the constraints up to a given level of probability. We show that, for a wide class of probability distributions (namely, radial distributions) on the data, the probability constraints can be converted explicitly into convex se...
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Introduction: A chance constrained optimization problem involves constraints with stochastic data that are required to be satisfied with a pre-specified probability. When the underlying distribution of the stochastic data is not known precisely, an often used model is to require the chance constraints to hold for all distributions in a given family. Such a problem is known as a distributionally...
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ژورنال
عنوان ژورنال: Mathematical Programming
سال: 2015
ISSN: 0025-5610,1436-4646
DOI: 10.1007/s10107-015-0896-z