Exact Augmented Lagrangian Duality for Nonconvex Mixed-Integer Nonlinear Optimization

Jointly with Martin Schmidt

Year: 2024

Abstract: In the context of mixed-integer nonlinear problems (MINLPs), it is well-known that strong duality does not hold in general if the standard Lagrangian dual is used. Hence, we consider the augmented Lagrangian dual (ALD), which adds a nonlinear penalty function to the classic Lagrangian function. For this setup, we study conditions under which the ALD leads to a zero duality gap for general MINLPs. In particular, under mild assumptions and for a large class of penalty functions, we show that the ALD yields zero duality gaps if the penalty parameter goes to infinity. If the penalty function is a norm, we also show that the ALD leads to zero duality gaps for a finite penalty parameter. Moreover, we show that such a finite penalty parameter can be computed in polynomial time in the mixed-integer linear case. This generalizes the recent results on linearly constrained mixed-integer problems by Bhardwaj et al. (2024), Boland and Eberhard (2014), Feizollahi et al. (2016), and Gu et al. (2020).

Cite as:

@techreport{lefebvre2024exact,
  title={Exact Augmented Lagrangian Duality for Nonconvex Mixed-Integer Nonlinear Optimization},
  author={Henri Lefebvre and Martin Schmidt},
  year={2024},
  archivePrefix={Optimization Online},
  url={https://optimization-online.org/?p=27046}
}

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Optimization Online

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