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A novel global optimization method based on an Augmented Lagrangian framework is introduced for continuous constrained nonlinear optimization problems. At each outer iteration k the method requires the $${\varepsilon_{k}}$$ -global minimization of the Augmented Lagrangian with simple constraints, where $${\varepsilon_k \to \varepsilon}$$ . Global convergence to an $${\varepsilon}$$ ...
Two Augmented Lagrangian algorithms for solving KKT systems are introduced. The algorithms differ in the way in which penalty parameters are updated. Possibly infeasible accumulation points are characterized. It is proved that feasible limit points that satisfy the Constant Positive Linear Dependence constraint qualification are KKT solutions. Boundedness of the penalty parameters is proved under...
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