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A branch-and-cut-and-price framework for convex MINLP applied to a stochastic network design problem
BibTeX
@MISC{Fortz_abranch-and-cut-and-price,
author = {Bernard Fortz and Martine Labbé and Michael Poss},
title = {A branch-and-cut-and-price framework for convex MINLP applied to a stochastic network design problem},
year = {}
}
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Abstract
Abstract. Many convex linearly constrained programs and mixed integer programs have a large number of variables, so that the variables should be generated dynamically throughout the solution algorithm. This yields to the well known "branch-and-price algorithm" and "simplicial decomposition". We present a novel "branch-and-cut-and-price algorithm" to extend this idea to certain classes of convex linearly constrained MINLP. Our algorithm incorporates the variables generation into the "LP/NLP algorithm" introduced by Quesada and Grossman. We detail our framework for the stochastic network design problem with simple recourse and present preliminary computational results.
