Mathematical Programming

, Volume 156, Issue 1–2, pp 125–159 | Cite as

A doubly stabilized bundle method for nonsmooth convex optimization

  • Welington de OliveiraEmail author
  • Mikhail Solodov
Full Length Paper Series A


We propose a bundle method for minimizing nonsmooth 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 1,000 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.


Nonsmooth optimization Proximal bundle method  Level bundle method Inexact oracle 

Mathematics Subject Classification

90C25 90C30 65K05 



The authors would like to acknowledge helpful comments of Claudia Sagastizábal. The authors also thank the anonymous referees for constructive suggestions that considerably improved the original version of this article.


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Copyright information

© Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society 2015

Authors and Affiliations

  1. 1.IMPA – Instituto de Matemática Pura e AplicadaRio de JaneiroBrazil

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