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

الملخص EN

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.