Abstract
The difference-of-convex (DC) decomposition is an effective method for designing a branch-and-bound algorithm. In this paper, we design two new branch-and-bound algorithms based on DC decomposition, to find global solutions of nonconvex box-constrained quadratic programming problems, and compare the efficiency of the proposed algorithms with two previous state-of-the-art branch-and-bound algorithms. Numerical experiments are conducted to show the competitiveness of the proposed algorithms on 20–60 dimensional box-constrained quadratic programming problems.
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Available at https://github.com/sburer/QUADPROGBB.
Available at http://sburer.github.io/files/Box-QP.tar.gz.
The BARON solver is called via the interface provided on the NEOS Server [10]. However, we believe that the significant difference of the performances between CP-BB and BARON is mainly due to algorithmic design, rather than the hardware of the machines.
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Lu’s research has been supported by National Natural Science Foundation of China Grant No. 11701177, Fundamental Research Funds for the Central Universities Grant No. 2017MS058. Deng’s research has been supported by National Natural Science Foundation of China Grant No. 11501543, and Research Foundation of UCAS Grants Nos. Y65201VY00 and Y65302V1G4.
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Lu, C., Deng, Z. DC decomposition based branch-and-bound algorithms for box-constrained quadratic programs. Optim Lett 12, 985–996 (2018). https://doi.org/10.1007/s11590-017-1203-0
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DOI: https://doi.org/10.1007/s11590-017-1203-0