Generalized Benders Decomposition Method to Solve Big Mixed-Integer Nonlinear Optimization Problems with Convex Objective and Constraints Functions

Andrzej Karbowski
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Andrzej Karbowski: Research and Academic Computer Network NASK—National Research Institute, ul. Kolska 12, 01-045 Warsaw, Poland

Energies, 2021, vol. 14, issue 20, 1-18

Abstract: The paper presents the Generalized Benders Decomposition (GBD) method, which is now one of the basic approaches to solve big mixed-integer nonlinear optimization problems. It concentrates on the basic formulation with convex objectives and constraints functions. Apart from the classical projection and representation theorems, a unified formulation of the master problem with nonlinear and linear cuts will be given. For the latter case the most effective and, at the same time, easy to implement computational algorithms will be pointed out.

Keywords: optimization; mixed-integer nonlinear programming; decomposition; convex problems; bilinear problems; integer programming; Generalized Benders Decomposition; hierarchical optimization; branch and cut (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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