Abstract
In this paper, a quadratically approximate algorithm framework for solving general constrained minimax problems is presented. The framework contains the idea of the sequential quadratic programming method, the sequential quadratically constrained quadratic programming method, norm-relaxed method and strong sub-feasible method. The global convergence of the algorithm framework is obtained under the Mangasarian–Fromovitz constraint qualification (MFCQ), and the conditions for superlinear convergence of the algorithm framework are presented under the MFCQ, the constant rank constraint qualification (CRCQ) as well as the strong second-order sufficiency conditions (SSOSC). And quadratic convergence rate is obtained under the MFCQ and SSOSC.
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.
