A two-stage feasible directions algorithm for nonlinear constrained optimization
Mathematical Programming: Series A and B
A nonmonotone line search technique for Newton's method
SIAM Journal on Numerical Analysis
SIAM Journal on Control and Optimization
On combining feasibility, descent and superlinear convergence in inequality constrained optimization
Mathematical Programming: Series A and B
On the Global Convergence of a Filter--SQP Algorithm
SIAM Journal on Optimization
Global Convergence of a Trust-Region SQP-Filter Algorithm for General Nonlinear Programming
SIAM Journal on Optimization
On the superlinear local convergence of a filter-SQP method
Mathematical Programming: Series A and B
A globally convergent primal-dual interior-point filter method for nonlinear programming
Mathematical Programming: Series A and B
Global and local convergence of a penalty-free method for nonlinear programming
Computers & Mathematics with Applications
Hi-index | 0.02 |
A framework for proving global convergence for a class of line search filter-type methods for nonlinear programming is presented without assuming that the Jacobian has full rank everywhere. The underlying method is based on the filter concept where trial points are accepted, provided there is a sufficient decrease in the objective function or constraints violation function. The proposed methods solve a sequence of quadratic programming subproblems via line search techniques to induce global convergence. Under mild conditions, we will also show that the algorithm converges two step superlinearly when the iterates are near to the solution.