Augmented Lagrangian Method with Alternating Constraints for Nonlinear Optimization Problems
- 216 Downloads
The augmented Lagrangian method is a classical solution method for nonlinear optimization problems. At each iteration, it minimizes an augmented Lagrangian function that consists of the constraint functions and the corresponding Lagrange multipliers. If the Lagrange multipliers in the augmented Lagrangian function are close to the exact Lagrange multipliers at an optimal solution, the method converges steadily. Since the conventional augmented Lagrangian method uses inaccurate estimated Lagrange multipliers, it sometimes converges slowly. In this paper, we propose a novel augmented Lagrangian method that allows the augmented Lagrangian function and its minimization problem to have variable constraints at each iteration. This allowance enables the new method to get more accurate estimated Lagrange multipliers by exploiting Karush–Kuhn–Tucker points of the subproblems and consequently to converge more efficiently and steadily.
KeywordsAugmented Lagrangian functions Gradient descent method Large-scale problem Nonlinear optimization
Mathematics Subject Classification26A16 41A25 47B36
- 3.Bartlett, P., Collins, M., Taskar, B., McAllester, D.: Exponentiated gradient algorithms for large-margin structured classification. In: Advances in Neural Information Processing Systems, vol. 17, pp. 113–120 (2004)Google Scholar