A successive quadratic programming algorithm with global and superlinear convergence properties
Mathematical Programming: Series A and B
A recursive quadratic programming algorithm that uses differentiable exact penalty functions
Mathematical Programming: Series A and B
SIAM Journal on Control and Optimization
SIAM Journal on Control and Optimization
On combining feasibility, descent and superlinear convergence in inequality constrained optimization
Mathematical Programming: Series A and B
Journal of Optimization Theory and Applications
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 Computationally Efficient Feasible Sequential Quadratic Programming Algorithm
SIAM Journal on Optimization
Hi-index | 7.29 |
In this paper, a new feasible sequential quadratic programming (FSQP) algorithm is proposed to solve the nonlinear programming, where a feasible descent direction is obtained by solving only one QP subproblem. In order to avoid Maratos effect, a high-order revised direction is computed by solving a linear system with involving some "active" constraints. The Theoretical analysis shows that global and superlinear convergence can be deduced.