Revisiting Augmented Lagrangian Duals
Claudia Sagastizábal (University of Campinas)
Abstract: For nonconvex optimization problems, possibly having mixed-integer variables, a convergent primal-dual solution algorithm is proposed. The approach applies a proximal bundle method to a certain augmented Lagrangian dual that arises in the context of the so-called generalized augmented Lagrangians. We recast these Lagrangians into the framework of a classical Lagrangian, by means of a special reformulation of the original problem. Thanks to this insight, the methodology yields zero duality gap. Lagrangian subproblems can be solved inexactly without hindering the primal-dual convergence properties of the algorithm. Primal convergence is ensured even when the dual solution set is empty. The interest of the new method is assessed on several problems, including unit commitment in energy optimization. These problems are solved to optimality by solving separable Lagrangian subproblems.
The talk is based on joint work with Marcelo Cordova and Welington de Oliveira.
optimization and control
Audience: researchers in the topic
Comments: the address and password of the zoom room of the seminar are sent by e-mail on the mailinglist of the seminar one day before each talk
One World Optimization seminar
Series comments: Description: Online seminar on optimization and related areas
The address and password of the zoom room of the seminar are sent by e-mail on the mailinglist of the seminar one day before each talk
| Organizers: | Sorin-Mihai Grad*, Radu Ioan Boț, Shoham Sabach, Mathias Staudigl |
| *contact for this listing |