Title | Nonconvex Generalized Benders Decomposition for Stochastic Separable Mixed-Integer Nonlinear Programs |
Publication Type | Journal Article |
Year of Publication | 2011 |
Authors | Li X, Tomasgard A, Barton PI |
Journal | Journal of Optimization Theory and Applications |
Volume | 151 |
Pagination | 425-454 |
ISSN | 0022-3239 |
Keywords | Decomposition algorithm, global optimization, Mixed-integer nonlinear programming, stochastic programming |
Abstract | This paper considers deterministic global optimization of scenario-based, two-stage stochastic mixed-integer nonlinear programs (MINLPs) in which the participating functions are nonconvex and separable in integer and continuous variables. A novel decomposition method based on generalized Benders decomposition, named nonconvex generalized Benders decomposition (NGBD), is developed to obtain ε-optimal solutions of the stochastic MINLPs of interest in finite time. The dramatic computational advantage of NGBD over state-of-the-art global optimizers is demonstrated through the computational study of several engineering problems, where a problem with almost 150,000 variables is solved by NGBD within 80 minutes of solver time. |
URL | http://dx.doi.org/10.1007/s10957-011-9888-1 |
DOI | 10.1007/s10957-011-9888-1 |
Nonconvex Generalized Benders Decomposition for Stochastic Separable Mixed-Integer Nonlinear Programs
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