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Nonsmooth and Nonconvex Optimization via Approximate Difference-of-Convex Decompositions

  • Wim van Ackooij
  • Welington de OliveiraEmail author
Article

Abstract

We propose an optimization technique for computing stationary points of a broad class of nonsmooth and nonconvex programming problems. The proposed approach (approximately) decomposes the objective function as the difference of two convex functions and performs inexact optimization of the resulting (convex) subproblems. We prove global convergence of our method in the sense that, for an arbitrary starting point, every accumulation point of the sequence of iterates is a Clarke-stationary solution. The given approach is validated by encouraging numerical results on several nonsmooth and nonconvex distributionally robust optimization problems.

Keywords

Nonconvex programming Nonsmooth optimization Lower-\(C^2\) functions DC decomposition 

Mathematics Subject Classification

49J52 49J53 49K99 90C26 

Notes

Acknowledgements

The authors would like to acknowledge financial support from the Gaspard-Monge program for Optimization and Operations Research (PGMO) project “Optimization & stability of stochastic unit-commitment problems.”

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.EDF R&DÉlectricité de FranceParisFrance
  2. 2.MINES ParisTech, PSL – Research University, CMA – Centre de Mathématiques AppliquéesSophia AntipolisFrance

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