Nonlinear programming: theory, algorithms, and applications
Nonlinear programming: theory, algorithms, and applications
A truncated Newton method with nonmonotone line search for unconstrained optimization
Journal of Optimization Theory and Applications
Journal of Optimization Theory and Applications
Nonmonotone curvilinear line search methods for unconstrained optimization
Computational Optimization and Applications
Journal of Optimization Theory and Applications
Convergence to Second Order Stationary Points in Inequality Constrained Optimization
Mathematics of Operations Research
Trust-Region Interior-Point SQP Algorithms for a Class of Nonlinear Programming Problems
SIAM Journal on Control and Optimization
An Augmented Lagrangian Function with Improved Exactness Properties
SIAM Journal on Optimization
On the Convergence Theory of Trust-Region-Based Algorithms for Equality-Constrained Optimization
SIAM Journal on Optimization
Newton Methods For Large-Scale Linear Inequality-Constrained Minimization
SIAM Journal on Optimization
On the Accurate Identification of Active Constraints
SIAM Journal on Optimization
An Interior Point Algorithm for Large-Scale Nonlinear Programming
SIAM Journal on Optimization
SIAM Journal on Optimization
Convergent Infeasible Interior-Point Trust-Region Methods for Constrained Minimization
SIAM Journal on Optimization
A New Trust-Region Algorithm for Equality Constrained Optimization
Computational Optimization and Applications
An Exact Augmented Lagrangian Function for Nonlinear Programming with Two-Sided Constraints
Computational Optimization and Applications
Second-order negative-curvature methods for box-constrained and general constrained optimization
Computational Optimization and Applications
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We define a primal-dual algorithm model (second-order Lagrangian algorithm, SOLA) for inequality constrained optimization problems that generates a sequence converging to points satisfying the second-order necessary conditions for optimality. This property can be enforced by combining the equivalence between the original constrained problem and the unconstrained minimization of an exact augmented Lagrangian function and the use of a curvilinear line search technique that exploits information on the nonconvexity of the augmented Lagrangian function.