Analysis and implementation of a dual algorithm for constrained optimization
Journal of Optimization Theory and Applications
Trust-region methods
On the Global Convergence of a Filter--SQP Algorithm
SIAM Journal on Optimization
On the Constant Positive Linear Dependence Condition and Its Application to SQP Methods
SIAM Journal on Optimization
Global Convergence of a Trust-Region SQP-Filter Algorithm for General Nonlinear Programming
SIAM Journal on Optimization
Large-Scale Active-Set Box-Constrained Optimization Method with Spectral Projected Gradients
Computational Optimization and Applications
Interior-Point Methods for Nonconvex Nonlinear Programming: Filter Methods and Merit Functions
Computational Optimization and Applications
Mathematical Programming: Series A and B
On Augmented Lagrangian Methods with General Lower-Level Constraints
SIAM Journal on Optimization
Improving ultimate convergence of an augmented Lagrangian method
Optimization Methods & Software - Dedicated to Professor Michael J.D. Powell on the occasion of his 70th birthday
Optimal Quadratic Programming Algorithms: With Applications to Variational Inequalities
Optimal Quadratic Programming Algorithms: With Applications to Variational Inequalities
Infeasibility Detection and SQP Methods for Nonlinear Optimization
SIAM Journal on Optimization
A New Sequential Optimality Condition for Constrained Optimization and Algorithmic Consequences
SIAM Journal on Optimization
Journal of Global Optimization
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Practical Nonlinear Programming algorithms may converge to infeasible points. It is sensible to detect this situation as quickly as possible, in order to have time to change initial approximations and parameters, with the aim of obtaining convergence to acceptable solutions in further runs. In this paper, a recently introduced Augmented Lagrangian algorithm is modified in such a way that the probability of quick detection of asymptotic infeasibility is enhanced. The modified algorithm preserves the property of convergence to stationary points of the sum of squares of infeasibilities without harming the convergence to KKT points in feasible cases.