Enhancing Semidefinite Relaxation for Quadratically Constrained Quadratic Programming via Penalty Methods

Quadratically constrained quadratic programming arises from a broad range of applications and is known to be among the hardest optimization problems. In recent years, semidefinite relaxation has become a popular approach for quadratically constrained quadratic programming, and many results have been...

Full description

Saved in:

Bibliographic Details
Published in:	Journal of optimization theory and applications Vol. 180; no. 3; pp. 964 - 992
Main Authors:	Luo, Hezhi, Bai, Xiaodi, Peng, Jiming
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.03.2019 Springer Nature B.V
Subjects:	Applications of Mathematics Calculus of Variations and Optimal Control; Optimization Engineering Iterative methods Linear programming Mathematical models Mathematics Mathematics and Statistics Operations Research/Decision Theory Optimization Penalty function Quadratic programming Theory of Computation Conditionally quasi-convex relaxation 90C20 Bisection search 90C22 Iterative search 90C26 Quadratic programming Penalty method
ISSN:	0022-3239, 1573-2878
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!