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

Xiaoqing Wang | Zendy

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

Author(s) -

Xiaoqing Wang

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/896591

Subject(s) - conic section , duality (order theory) , strong duality , conic optimization , mathematical optimization , discretization , robust optimization , dual (grammatical number) , mathematics , quadratic programming , linear programming , stochastic programming , sign (mathematics) , class (philosophy) , extension (predicate logic) , optimization problem , computer science , convex optimization , discrete mathematics , regular polygon , convex analysis , mathematical analysis , art , geometry , literature , artificial intelligence , programming language

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

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