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Two-sided linear chance constraints and extensions

Miles Lubin(mlubin***at***mit.edu)
Juan Pablo Vielma(jvielma***at***mit.edu)
Daniel Bienstock(dano***at***columbia.edu)

Abstract: We examine the convexity and tractability of the two-sided linear chance constraint model under Gaussian uncertainty. We show that these constraints can be applied directly to model a larger class of nonlinear chance constraints as well as provide a reasonable approximation for a challenging class of quadratic chance constraints of direct interest for applications in power systems. With a view towards practical computations, we develop a second-order cone outer approximation of the two-sided chance constraint with provably small approximation error.


Category 1: Convex and Nonsmooth Optimization (Convex Optimization )

Category 2: Stochastic Programming

Category 3: Linear, Cone and Semidefinite Programming (Second-Order Cone Programming )

Citation: http://arxiv.org/abs/1507.01995

Download: [PDF]

Entry Submitted: 02/27/2016
Entry Accepted: 02/27/2016
Entry Last Modified: 02/27/2016

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