Bibliographic Details
| Title: |
An Efficient Branch‐and‐Bound Algorithm for Globally Minimizing a Class of Generalized Linear Multiplicative Programs. |
| Authors: |
Hu, Peng, Wu, Zhiyou, Yang, Tao, Liu, Jia, Xin, Bangying, Zhou, Ding-Xuan |
| Source: |
Journal of Mathematics; 10/9/2025, Vol. 2025, p1-19, 19p |
| Subject Terms: |
Global optimization, Mathematical optimization, Nonlinear programming, Convex programming, Piecewise linear approximation, Empirical research |
| Abstract: |
This study presents a novel algorithm for globally solving generalized linear multiplicative programming (GLMP) problems. We first introduce a convex‐separation technique to craft a tight yet computationally tractable linear relaxation that supplies strong lower bounds for the original nonconvex formulation. Building upon this relaxation, a rigorous branch‐and‐bound framework is designed, and its global convergence is proved along with a comprehensive complexity analysis. Extensive numerical experiments demonstrate that the proposed algorithm significantly outperforms existing methods in both computational efficiency and robustness. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |