On semidefinite programming relaxations for a class of robust SOS-convex polynomial optimization problems

In this paper, we deal with a new class of SOS-convex (sum of squares convex) polynomial optimization problems with spectrahedral uncertainty data in both the objective and constraints. By using robust optimization and a weighted-sum scalarization methodology, we first present the relationship betwe...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Journal of global optimization Ročník 88; číslo 3; s. 755 - 776
Hlavní autori:	Sun, Xiangkai, Huang, Jiayi, Teo, Kok Lay
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.03.2024 Springer Springer Nature B.V
Predmet:	Computer Science Convex analysis Linear matrix inequalities Mathematical analysis Mathematics Mathematics and Statistics Operations Research/Decision Theory Optimization Polynomials Programming Real Functions Robustness Semidefinite programming Convex polynomial optimization Optimality conditions 90C29 Semidefinite programming relaxations 90C46 90C25
ISSN:	0925-5001, 1573-2916
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!