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...

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Vydané v:	Journal of optimization theory and applications Ročník 180; číslo 3; s. 964 - 992
Hlavní autori:	Luo, Hezhi, Bai, Xiaodi, Peng, Jiming
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.03.2019 Springer Nature B.V
Predmet:	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
On-line prístup:	Získať plný text
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