期刊
MATHEMATICS OF OPERATIONS RESEARCH
卷 34, 期 1, 页码 1-25出版社
INFORMS
DOI: 10.1287/moor.1080.0352
关键词
chance constraints; linear matrix inequalities; convex programming; measure concentration
资金
- BSF [2002038]
- NSF [0619977]
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [0619977] Funding Source: National Science Foundation
In the paper we consider the chance-constrained version of an affinely perturbed linear matrix inequality (LMI) constraint, assuming the primitive perturbations to be independent with light-tail distributions (e.g., bounded or Gaussian). Constraints of this type, playing a central role in chance-constrained linear/conic quadratic/semidefinite programming, are typically computationally intractable. The goal of this paper is to develop a tractable approximation to these chance constraints. Our approximation is based on measure concentration results and is given by an explicit system of LMIs. Thus, the approximation is computationally tractable; moreover, it is also safe, meaning that a feasible solution of the approximation is feasible for the chance constraint.
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