Boyu Han, Xu Du, Karl H. Johansson, Apostolos I. Rikos
Accepted by IFAC World Congress 2026
This paper addresses distributed consensus optimization problems with mixedinteger variables, with a specific focus on Boolean variables. We introduce a novel distributed algorithm that extends the Consensus Augmented Lagrangian Alternating Direction Inexact Newton (CALADIN) framework by incorporating specialized techniques for handling Boolean variables without relying on local mixed-integer solvers. Under the mild assumption of Lipschitz continuity of the objective functions, we establish rigorous convergence guarantees for both convex and nonconvex mixed-integer programming problems. Numerical experiments demonstrate that the proposed algorithm achieves competitive performance compared to existing approaches while providing rigorous convergence guarantees.
@article{han2026mix,
title={Mix-{CALADIN}: {A} distributed algorithm for consensus mixed-integer optimization},
author={Han, Boyu and Du, Xu and Johansson, Karl H and Rikos, Apostolos I},
journal={arXiv preprint arXiv:2604.14897},
year={2026}
}