On the convexity of probabilitistically constrained linear programs

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    Abstract

    The focal point of this paper is the so-called Probabilistically Constrained Linear Program (PCLP), also known as Chance Constrained Linear Program. We prove that PCLP is a convex program when the uncertain parameters are uniformly distributed over a convex symmetric set Q. By symmetric we mean that if q qq Q then -q qq Q. Furthermore, we provide a deterministic equivalent of the PCLP which, in some cases, leads to an implementation that does not require the use of Stochastic Programming; only commonly available optimization tools are required. Finally, the concept of Probabilistically Robust Linear Program (PRLP) is introduced. The PRLP is a generalization of the PCLP for the case when the distribution of the uncertain coefficients is not known. The only assumption is that their distribution belongs to a known class F. Since the true distribution of the uncertain parameters is not available, in the PRLP it is required that the probability of satisfaction of the constraints is greater than a given risk level ε for all possible distributions f qq F. It is proven that the PRLP is also a convex program for the class F first introduced in [3].

    Original languageEnglish (US)
    Pages (from-to)516-521
    Number of pages6
    JournalProceedings of the IEEE Conference on Decision and Control
    Volume1
    StatePublished - Jan 1 1999
    EventThe 38th IEEE Conference on Decision and Control (CDC) - Phoenix, AZ, USA
    Duration: Dec 7 1999Dec 10 1999

    All Science Journal Classification (ASJC) codes

    • Control and Systems Engineering
    • Modeling and Simulation
    • Control and Optimization

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