A Decomposition Method for Large Scale MILPs, with Performance Guarantees and a Power System Application

@article{Vujanic2016ADM,
  title={A Decomposition Method for Large Scale MILPs, with Performance Guarantees and a Power System Application},
  author={Robin Vujanic and Peyman Mohajerin Esfahani and Paul J. Goulart and S{\'e}bastien Mari{\'e}thoz and Manfred Morari},
  journal={Automatica},
  year={2016},
  volume={67},
  pages={144-156}
}
Lagrangian duality in mixed integer optimization is a useful framework for problems decomposition and for producing tight lower bounds to the optimal objective, but in contrast to the convex counterpart, it is generally unable to produce optimal solutions directly. In fact, solutions recovered from the dual may be not only suboptimal, but even infeasible. In this paper we concentrate on large scale mixed–integer programs with a specific structure that is of practical interest, as it appears in… CONTINUE READING