Stochastic Separated Continuous Conic Programming: Strong Duality and a Solution Method

We study a new class of optimization problems called stochastic separated continuous conic programming (SSCCP). SSCCP is an extension to the optimization model called separated continuous conic programming (SCCP) which has applications in robust optimization and sign-constrained linear-quadratic con...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Mathematical problems in engineering Ročník 2014; číslo 1
Hlavný autor: Wang, Xiaoqing
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Hindawi Publishing Corporation 01.01.2014
John Wiley & Sons, Inc
Predmet:
Algorithms
Approximation
Conics
Discretization
Euclidean space
Linear programming
Mathematical analysis
Mathematical models
Mathematical problems
Optimization
Optimization models
Polynomials
Programming
Random variables
Robust control
Stochasticity
ISSN:1024-123X, 1563-5147
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:We study a new class of optimization problems called stochastic separated continuous conic programming (SSCCP). SSCCP is an extension to the optimization model called separated continuous conic programming (SCCP) which has applications in robust optimization and sign-constrained linear-quadratic control. Based on the relationship among SSCCP, its dual, and their discretization counterparts, we develop a strong duality theory for the SSCCP. We also suggest a polynomial-time approximation algorithm that solves the SSCCP to any predefined accuracy.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Article-2
ObjectType-Feature-1
content type line 23
ISSN:1024-123X
1563-5147
DOI:10.1155/2014/896591