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Branch-and-Reduction Algorithm for Indefinite Quadratic Programming Problem
Author(s) -
Yongjian Qiu,
Yuming Zhu,
Jingben Yin
Publication year - 2021
Publication title -
complexity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.447
H-Index - 61
eISSN - 1099-0526
pISSN - 1076-2787
DOI - 10.1155/2021/5578427
Subject(s) - reduction (mathematics) , algorithm , relaxation (psychology) , bilinear interpolation , mathematical optimization , mathematics , quadratic programming , global optimization , branch and bound , sequence (biology) , linear programming , quadratic equation , computer science , psychology , social psychology , statistics , geometry , biology , genetics
This paper presents a rectangular branch-and-reduction algorithm for globally solving indefinite quadratic programming problem (IQPP), which has a wide application in engineering design and optimization. In this algorithm, first of all, we convert the IQPP into an equivalent bilinear optimization problem (EBOP). Next, a novel linearizing technique is presented for deriving the linear relaxation programs problem (LRPP) of the EBOP, which can be used to obtain the lower bound of the global optimal value to the EBOP. To obtain a global optimal solution of the EBOP, the main computational task of the proposed algorithm involves the solutions of a sequence of LRPP. Moreover, the global convergent property of the algorithm is proved, and numerical experiments demonstrate the higher computational performance of the algorithm.

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