Abstract
Abstract : Semidefinite programs are a class of optimization problems that have been the focus of intense research during the past fifteen years. Semidefinite programs extend linear programs, and both are defined using deterministic data. However, uncertainty is naturally present in applications leading to optimization problems. Stochastic linear programs with recourse have been studied since the fifties as a way to deal with uncertainty in data defining linear programs. Recently, the authors have defined an analogous extension of semidefinite programs termed stochastic semidefinite programs with recourse to deal with uncertainty in data defining semidefinite programs. A prominent alternative for handling uncertainty in data defining linear programs is chance-constrained linear programming. In this paper we introduce an analogous extension of semidefinite programs termed chance-constrained semidefinite programs for handling uncertainty in data defining semidefinite programs.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
