A Superlinearly feasible SQP algorithm for Constrained Optimization
نویسندگان
چکیده
This paper is concerned with a Superlinearly feasible SQP algorithm algorithm for general constrained optimization. As compared with the existing SQP methods, it is necessary to solve equality constrained quadratic programming sub-problems at each iteration, which shows that the computational effort of the proposed algorithm is reduced further. Furthermore, under some mild assumptions, the algorithm is globally convergent and its rate of convergence is one-step superlinearly.
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