Generalized Benders Decomposition for one Class of MINLPs with Vector Conic Constraint
Optimization and Control
2015-09-15 v1
Abstract
In this paper, we mainly study one class of mixed-integer nonlinear programming problems (MINLPs) with vector conic constraint in Banach spaces. Duality theory of convex vector optimization problems applied to this class of MINLPs is deeply investigated. With the help of duality, we use the generalized Benders decomposition method to establish an algorithm for solving this MINLP. Several convergence theorems on the algorithm are also presented. The convergence theorems generalize and extend the existing results on MINLPs in finite dimension spaces.
Keywords
Cite
@article{arxiv.1506.08091,
title = {Generalized Benders Decomposition for one Class of MINLPs with Vector Conic Constraint},
author = {Zhou Wei and M. Montaz Ali},
journal= {arXiv preprint arXiv:1506.08091},
year = {2015}
}
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20 pages