Globally convergent algorithm for nonlinear constrained optimization problems
Journal of Optimization Theory and Applications
A robust sequential quadratic programming method
Mathematical Programming: Series A and B
A generalization of the norm-relaxed method of feasible directions
Applied Mathematics and Computation
A superlinearly convergent method of feasible directions
Applied Mathematics and Computation
Test Examples for Nonlinear Programming Codes
Test Examples for Nonlinear Programming Codes
A Robust Algorithm for Optimization with General Equality and Inequality Constraints
SIAM Journal on Scientific Computing
A Modified SQP Method and Its Global Convergence
Journal of Global Optimization
Hi-index | 7.29 |
In this paper, a variant of SQP method for solving inequality constrained optimization is presented. This method uses a modified QP subproblem to generate a descent direction as each iteration and can overcome the possible difficulties that the QP subproblem of the standard SQP method is inconsistency. Furthermore, the method can start with an infeasible initial point. Under mild conditions, we prove that the algorithm either terminates as KKT point within finite steps or generates an infinite sequence whose accumulation point is a KKT point or satisfies certain first-order necessary condition. Finally, preliminary numerical results are reported.