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...

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Published in:Mathematical problems in engineering Vol. 2014; no. 1
Main Author: Wang, Xiaoqing
Format: Journal Article
Language:English
Published: New York Hindawi Publishing Corporation 01.01.2014
John Wiley & Sons, Inc
Subjects:
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
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Abstract 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.
AbstractList 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.
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.
Author Wang, Xiaoqing
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ContentType Journal Article
Copyright Copyright © 2014 Xiaoqing Wang.
Copyright © 2014 Xiaoqing Wang. Xiaoqing Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright_xml – notice: Copyright © 2014 Xiaoqing Wang.
– notice: Copyright © 2014 Xiaoqing Wang. Xiaoqing Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Snippet We study a new class of optimization problems called stochastic separated continuous conic programming (SSCCP). SSCCP is an extension to the optimization model...
We study a new class of optimization problems called stochastic separated continuous conic programming (SSCCP). SSCCP is an extension to the optimization model...
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SubjectTerms Algorithms
Approximation
Conics
Discretization
Euclidean space
Linear programming
Mathematical analysis
Mathematical models
Mathematical problems
Optimization
Optimization models
Polynomials
Programming
Random variables
Robust control
Stochasticity
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Title Stochastic Separated Continuous Conic Programming: Strong Duality and a Solution Method
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Volume 2014
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