ABSTRACT
In this paper, an adaptation of the sequential quadratically constrained quadratic programming method is proposed to solve inequality constrained minimization. At each iteration, it solves a norm-relaxed quadratically constrained quadratic programming subproblem that uses an active set identification technique to reduce the scale and computational cost. By taking a valid line search, the iterates always get into the feasible set after a finite number of iterations, and followed by a suitable update rule for the penalty parameters. Under suitable conditions without the strict complementarity, our method has the global and superlinear convergence properties. In addition, numerical results are reported to demonstrate the efficiency of the proposed method.
Acknowledgements
The authors are grateful to the editors and anonymous referees for their valuable comments and suggestions to improve the earlier draft of this paper.
Disclosure statement
No potential conflict of interest was reported by the authors.