Abstract
Dynamic programming is one of the methods which utilize special structures of large-scale mathematical programming problems. Conventional dynamic programming, however, can hardly solve mathematical programming problems with many constraints. This paper proposes differential dynamic programming algorithms for solving large-scale nonlinear programming problems with many constraints and proves their local convergence. The present algorithms, based upon Kuhn-Tucker conditions for subproblems decomposed by dynamic programming, are composed of iterative methods for solving systems of nonlinear equations. It is shown that the convergence of the present algorithms with Newton's method is R-quadratic. Three numerical examples including the Rosen-Suzuki test problem show the efficiency of the present algorithms.
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 callMore From: Journal of the Operations Research Society of Japan
Disclaimer: 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.
