A recursive quadratic programming algorithm that uses differentiable exact penalty functions
Mathematical Programming: Series A and B
Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
A robust sequential quadratic programming method
Mathematical Programming: Series A and B
Globally convergent Newton methods for nonsmooth equations
Mathematics of Operations Research
A Robust Algorithm for Optimization with General Equality and Inequality Constraints
SIAM Journal on Scientific Computing
SIAM Journal on Optimization
Primal-Dual Active Set Strategy for a General Class of Constrained Optimal Control Problems
SIAM Journal on Optimization
A Decomposition Method Based on SQP for a Class of Multistage Stochastic Nonlinear Programs
SIAM Journal on Optimization
Hi-index | 7.29 |
Sequential quadratic programming (SQP) has been one of the most important methods for solving nonlinearly constrained optimization problems. In this paper, we present and study an active set SQP algorithm for inequality constrained optimization. The active set technique is introduced which results in the size reduction of quadratic programming (QP) subproblems. The algorithm is proved to be globally convergent. Thus, the results show that the global convergence of SQP is still guaranteed by deleting some ''redundant'' constraints.