Mathematical Programming: Series A and B
On the formulation and theory of the Newton interior-point method for nonlinear programming
Journal of Optimization Theory and Applications
Mathematical Programming: Series A and B
Superlinearly convergent infeasible-interior-point algorithm for degenerate LCP
Journal of Optimization Theory and Applications
Superlinear Convergence of a Stabilized SQP Method to a Degenerate Solution
Computational Optimization and Applications
An interface optimization and application for the numerical solution of optimal control problems
ACM Transactions on Mathematical Software (TOMS)
Superlinear Convergence of an Interior-Point Method Despite Dependent Constraints
Mathematics of Operations Research
Local Convergence of the Affine-Scaling Interior-Point Algorithm for Nonlinear Programming
Computational Optimization and Applications
On the Accurate Identification of Active Constraints
SIAM Journal on Optimization
An Interior Point Algorithm for Large-Scale Nonlinear Programming
SIAM Journal on Optimization
Primal-Dual Interior Methods for Nonconvex Nonlinear Programming
SIAM Journal on Optimization
Computational Optimization and Applications
Local analysis of the feasible primal-dual interior-point method
Computational Optimization and Applications
Dynamic updates of the barrier parameter in primal-dual methods for nonlinear programming
Computational Optimization and Applications
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In recent work, the local convergence behavior of path-following interior-point methods and sequential quadratic programming methods for nonlinear programming has been investigated for the case in which the assumption of linear independence of the active constraint gradients at the solution is replaced by the weaker Mangasarian–Fromovitz constraint qualification. In this paper, we describe a stabilization of the primal-dual interior-point approach that ensures rapid local convergence under these conditions without enforcing the usual centrality condition associated with path-following methods. The stabilization takes the form of perturbations to the coefficient matrix in the step equations that vanish as the iterates converge to the solution.