Practical augmented Lagrangian methods for constrained optimization Nomenclature Introduction Practical motivations Optimality conditions Model augmented lagrangian algorithm Global minimization approach General affordable algorithms Boundedness of the penalty parameters Solving unconstrained subproblems Solving constrained subproblems First approach to algencan Adequate choice of subroutines Making a good choice of algorithmic options and parameters Practical examples Final remarks