Abstract:
We propose a bundle method for minimizing non smooth convex functions that
combines both the level and the proximal stabilizations. Most bundle algorithms use a
cutting-plane model of the objective function to formulate a subproblem whose solution
gives the next iterate. Proximal bundle methods employ the model in the objective function of the subproblem, while level methods put the model in the subproblem’s constraints. The proposed algorithm defines new iterates by solving a subproblem that employs the model in both the objective function and in the constraints. One advantage when compared to the proximal approach is that the level set constraint provides a certain Lagrange multiplier, which is used to update the proximal parameter in a novel manner. We also show that in the case of inexact function and subgradient evaluations, no additional procedure needs to be performed by our variant to deal with inexactness (as opposed to the proximal bundle methods that require special modifications). Numerical experiments on almost one thousand instances of different types of problems are presented. Our experiments show that the doubly stabilized bundle method inherits useful features of the level and the proximal versions, and compares favorably to both of them.
