Functional constrained optimization is becoming more and more important in machine
learning and operations research. Such problems have potential applications in risk-averse …

This paper is devoted to the theoretical and numerical investigation of an augmented
Lagrangian method for the solution of optimization problems with geometric constraints …

Algencan is a well established safeguarded Augmented Lagrangian algorithm introduced in
[R. Andreani, EG Birgin, JM Martínez, and ML Schuverdt, On Augmented Lagrangian …

Recently, a new approach to tackle cardinality-constrained optimization problems based on
a continuous reformulation of the problem was proposed. Following this approach, we …

In recent years, the theoretical convergence of iterative methods for solving nonlinear
constrained optimization problems has been addressed using sequential optimality …

In the present paper, we prove that the augmented Lagrangian method converges to KKT
points under the quasi-normality constraint qualification, which is associated with the …

Based on the tools of limiting variational analysis, we derive a sequential necessary
optimality condition for nonsmooth mathematical programs which holds without any …

Sequential optimality conditions have played a major role in unifying and extending global
convergence results for several classes of algorithms for general nonlinear optimization. In …

At each iteration of the safeguarded augmented Lagrangian algorithm Algencan, a bound-
constrained subproblem consisting of the minimization of the Powell–Hestenes–Rockafellar …

Generalized Nash equilibrium problems (GNEPs) are a generalization of the classic Nash
equilibrium problems (NEPs), where each player's strategy set depends on the choices of …