Branch-delete-bound algorithm for globally solving quadratically constrained quadratic programs

Zhisong Hou, et al. "Branch-delete-bound algorithm for globally solving quadratically constrained quadratic programs." Open Mathematics 15.1 (2017): 1212-1224. <http://eudml.org/doc/288510>.

@article{ZhisongHou2017,
abstract = {This paper presents a branch-delete-bound algorithm for effectively solving the global minimum of quadratically constrained quadratic programs problem, which may be nonconvex. By utilizing the characteristics of quadratic function, we construct a new linearizing method, so that the quadratically constrained quadratic programs problem can be converted into a linear relaxed programs problem. Moreover, the established linear relaxed programs problem is embedded within a branch-and-bound framework without introducing any new variables and constrained functions, which can be easily solved by any effective linear programs algorithms. By subsequently solving a series of linear relaxed programs problems, the proposed algorithm can converge the global minimum of the initial quadratically constrained quadratic programs problem. Compared with the known methods, numerical results demonstrate that the proposed method has higher computational efficiency.},
author = {Zhisong Hou, Hongwei Jiao, Lei Cai, Chunyang Bai},
journal = {Open Mathematics},
keywords = {Quadratically constrained quadratic programs; Global optimization; Linearizing method; Deleting technique; Branch-delete-bound algorithm},
language = {eng},
number = {1},
pages = {1212-1224},
title = {Branch-delete-bound algorithm for globally solving quadratically constrained quadratic programs},
url = {http://eudml.org/doc/288510},
volume = {15},
year = {2017},
}

TY - JOUR
AU - Zhisong Hou
AU - Hongwei Jiao
AU - Lei Cai
AU - Chunyang Bai
TI - Branch-delete-bound algorithm for globally solving quadratically constrained quadratic programs
JO - Open Mathematics
PY - 2017
VL - 15
IS - 1
SP - 1212
EP - 1224
AB - This paper presents a branch-delete-bound algorithm for effectively solving the global minimum of quadratically constrained quadratic programs problem, which may be nonconvex. By utilizing the characteristics of quadratic function, we construct a new linearizing method, so that the quadratically constrained quadratic programs problem can be converted into a linear relaxed programs problem. Moreover, the established linear relaxed programs problem is embedded within a branch-and-bound framework without introducing any new variables and constrained functions, which can be easily solved by any effective linear programs algorithms. By subsequently solving a series of linear relaxed programs problems, the proposed algorithm can converge the global minimum of the initial quadratically constrained quadratic programs problem. Compared with the known methods, numerical results demonstrate that the proposed method has higher computational efficiency.
LA - eng
KW - Quadratically constrained quadratic programs; Global optimization; Linearizing method; Deleting technique; Branch-delete-bound algorithm
UR - http://eudml.org/doc/288510
ER -