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A parallelizable augmented Lagrangian method applied to large-scale non-convex-constrained optimization problems

  • Natashia Boland
  • Jeffrey Christiansen
  • Brian Dandurand
  • Andrew Eberhard
  • Fabricio Oliveira
Full Length Paper Series A

Abstract

We contribute improvements to a Lagrangian dual solution approach applied to large-scale optimization problems whose objective functions are convex, continuously differentiable and possibly nonlinear, while the non-relaxed constraint set is compact but not necessarily convex. Such problems arise, for example, in the split-variable deterministic reformulation of stochastic mixed-integer optimization problems. We adapt the augmented Lagrangian method framework to address the presence of nonconvexity in the non-relaxed constraint set and to enable efficient parallelization. The development of our approach is most naturally compared with the development of proximal bundle methods and especially with their use of serious step conditions. However, deviations from these developments allow for an improvement in efficiency with which parallelization can be utilized. Pivotal in our modification to the augmented Lagrangian method is an integration of the simplicial decomposition method and the nonlinear block Gauss–Seidel method. An adaptation of a serious step condition associated with proximal bundle methods allows for the approximation tolerance to be automatically adjusted. Under mild conditions optimal dual convergence is proven, and we report computational results on test instances from the stochastic optimization literature. We demonstrate improvement in parallel speedup over a baseline parallel approach.

Keywords

Augmented Lagrangian method Proximal bundle method Nonlinear block Gauss–Seidel method Simplicial decomposition method Parallel computing 

Mathematics Subject Classification

90-08 90C06 90C11 90C15 90C25 90C26 90C30 90C46 

Supplementary material

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10107_2018_1253_MOESM1_ESM.pdf (88 kb)
Supplementary material 1 (pdf 87 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature and Mathematical Optimization Society 2018

Authors and Affiliations

  • Natashia Boland
    • 1
  • Jeffrey Christiansen
    • 2
  • Brian Dandurand
    • 2
  • Andrew Eberhard
    • 2
  • Fabricio Oliveira
    • 3
  1. 1.Georgia Institute of TechnologyAtlantaUSA
  2. 2.RMIT UniversityMelbourneAustralia
  3. 3.Aalto UniversityEspooFinland

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