Abstract
In recent years it has been proposed by a number of authors that convex duality theory could be applied to non-convex programming problems by generalizing the traditional Lagrangian function. Those results, having close relations to the computational technique for solving a problem via its dual, are very important. In this paper a class of generalized Lagrangian functions, called multiplier functions, is defined and non-convex duality theory associated with this function is developed for a more general problem constrained by a set as well as inequality constraints. These results give a complete extension of convex duality theory (saddle-point result) to non-convex programming in a finite dimensional space.
Sign-in/Register to access full text options 
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.
