In this paper, a novel sharp Augmented Lagrangian-based global optimization method is developed for solving constrained non-convex optimization problems. The algorithm consists of outer and inner loops. At each inner iteration, the discrete gradient method is applied to minimize the sharp augmented Lagrangian function. Depending on the solution found the algorithm stops or updates the dual variables in the inner loop, or updates the upper or lower bounds by going to the outer loop. The convergence results for the proposed method are presented. The performance of the method is demonstrated using a wide range of nonlinear smooth and non-smooth constrained optimization test problems from the literature
The field of constrained non-linear optimization is one of the most practically applicable areas of ...
At each outer iteration of standard Augmented Lagrangian methods one tries to solve a box-constraine...
At each outer iteration of standard Augmented Lagrangian methods one tries to solve a box-constraine...
In this paper, a novel sharp Augmented Lagrangian-based global optimization method is developed for ...
In this paper, a novel sharp Augmented Lagrangian-based global optimization method is developed for ...
A novel global optimization method based on an Augmented Lagrangian framework is introduced for cont...
A novel global optimization method based on an Augmented Lagrangian framework is introduced for cont...
The Lagrangian function in the conventional theory for solving constrained optimization problems is ...
A novel global optimization method based on an Augmented Lagrangian framework is introduced for cont...
A novel global optimization method based on an Augmented Lagrangian framework is introduced for cont...
A novel global optimization method based on an Augmented Lagrangian framework is introduced for cont...
Abstract. The global and local convergence properties of a class of augmented Lagrangian methods for...
We consider a class of constrained optimization problems where the objective function is a sum of a ...
We consider the problem of minimizing a smooth nonconvex function over a structured convex feasible ...
Abstract. In this paper, we propose a smoothing augmented Lagrangian method for finding a stationary...
The field of constrained non-linear optimization is one of the most practically applicable areas of ...
At each outer iteration of standard Augmented Lagrangian methods one tries to solve a box-constraine...
At each outer iteration of standard Augmented Lagrangian methods one tries to solve a box-constraine...
In this paper, a novel sharp Augmented Lagrangian-based global optimization method is developed for ...
In this paper, a novel sharp Augmented Lagrangian-based global optimization method is developed for ...
A novel global optimization method based on an Augmented Lagrangian framework is introduced for cont...
A novel global optimization method based on an Augmented Lagrangian framework is introduced for cont...
The Lagrangian function in the conventional theory for solving constrained optimization problems is ...
A novel global optimization method based on an Augmented Lagrangian framework is introduced for cont...
A novel global optimization method based on an Augmented Lagrangian framework is introduced for cont...
A novel global optimization method based on an Augmented Lagrangian framework is introduced for cont...
Abstract. The global and local convergence properties of a class of augmented Lagrangian methods for...
We consider a class of constrained optimization problems where the objective function is a sum of a ...
We consider the problem of minimizing a smooth nonconvex function over a structured convex feasible ...
Abstract. In this paper, we propose a smoothing augmented Lagrangian method for finding a stationary...
The field of constrained non-linear optimization is one of the most practically applicable areas of ...
At each outer iteration of standard Augmented Lagrangian methods one tries to solve a box-constraine...
At each outer iteration of standard Augmented Lagrangian methods one tries to solve a box-constraine...