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Convex relaxations of chance constrained optimization problems

Shabbir Ahmed (sahmed***at***isye.gatech.edu)

Abstract: In this paper we develop convex relaxations of chance constrained optimization problems in order to obtain lower bounds on the optimal value. Unlike existing statistical lower bounding techniques, our approach is designed to provide deterministic lower bounds. We show that a version of the proposed scheme leads to a tractable convex relaxation when the chance constraint function is affine with respect to the underlying random vector and the random vector has independent components. We also propose an iterative improvement scheme for refining the bounds.

Keywords: Chance constraints, Convex optimization

Category 1: Stochastic Programming

Citation: Working paper

Download: [PDF]

Entry Submitted: 06/24/2011
Entry Accepted: 06/24/2011
Entry Last Modified: 01/26/2012

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