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A parametric linear relaxation algorithm for globally solving nonconvex quadratic programming
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DOI：10.1016/j.amc.2014.11.032.png)
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Abstract
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In this article, we present a parametric linear relaxation algorithm for globally solving the nonconvex quadratic programming (NQP). In this algorithm, a new parametric linearized technique is proposed for generating parametric linear relaxation programming (PLRP) of the NQP, which can be used to determine the lower bound of global minimum value of the NQP. To improve the convergent speed of the proposed algorithm, a pruning operation is employed to compress the investigated region. By subdividing subsequently the initial domain and solving subsequently a series of parametric linear relaxation programming problems over the subdivided domain, the proposed algorithm is convergent to the global minimum of the NQP. Finally, an engineering problem for the design of heat exchanger network and some test examples are used to verify the effectiveness of the proposed algorithm. (C) 2014 Elsevier Inc. All rights reserved.
Keywords:
Nonconvex quadratic programming
Global optimization
Parametric linearized technique
Pruning operation
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