Stochastic separated continuous conic programming: strong duality and a solution method
Summary: We study a new class of optimization problems called \textit{stochastic separated continuous conic programming} (SSCCP). SSCCP is an extension to the optimization model called \textit{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.
scientific article; zbMATH DE number 7017536
scientific article