• DOI:10.1137/16M1076290
• Corpus ID: 16378122

Combining Progressive Hedging with a Frank-Wolfe Method to Compute Lagrangian Dual Bounds in Stochastic Mixed-Integer Programming

@article{Boland2018CombiningPH,
  title={Combining Progressive Hedging with a Frank-Wolfe Method to Compute Lagrangian Dual Bounds in Stochastic Mixed-Integer Programming},
  author={Natashia Boland and Jeffrey Christiansen and Brian C. Dandurand and Andrew Craig Eberhard and Jeff T. Linderoth and James R. Luedtke and Fabricio Oliveira},
  journal={SIAM J. Optim.},
  year={2018},
  volume={28},
  pages={1312-1336}
}

• N. Boland, J. Christiansen, +4 authors Fabricio Oliveira
• Published 3 February 2017
• Computer Science
• SIAM J. Optim.

We present a new primal-dual algorithm for computing the value of the Lagrangian dual of a stochastic mixed-integer program (SMIP) formed by relaxing its nonanticipativity constraints. This dual is widely used in decomposition methods for the solution of SMIPs. The algorithm relies on the well-known progressive hedging method, but unlike previous progressive hedging approaches for SMIP, our algorithm can be shown to converge to the optimal Lagrangian dual value. The key improvement in the new algorithm is an inner loop of optimized linearization steps, similar to those taken in the classical Frank--Wolfe method. Numerical results demonstrate that our new algorithm empirically outperforms the standard implementation of progressive hedging for obtaining bounds in SMIP.