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Generalized branch-and-cut framework for mixed-integer nonlinear optimization problems
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DOI：10.1016/S0098-1354(00)00421-X.png)
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Abstract
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Branch and bound (BB) is the primary deterministic approach that has been applied successfully to solve mixed-integer nonlinear programming (MINLPs) problems in which the participating functions are nonconvex. Recently, a decomposition algorithm was proposed to solve nonconvex MINLPs. In this work, a generalized branch and cut (GBC) algorithm is proposed and it is shown that both decomposition and BE algorithms are specific instances of the GBC algorithm with a certain set of heuristics. This provides a unified framework for comparing BE and decomposition algorithms. Finally, a set of heuristics which may be potentially more efficient computationally compared to all currently available deterministic algorithms is presented. (C) 2000 Elsevier Science Ltd. All rights reserved.
Keywords:
branch-and-cut algorithms
mixed-integer nonlinear optimization
decomposition heuristics
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