Mix-CALADIN: A Distributed Algorithm for Consensus Mixed-Integer Optimization
Abstract
This paper addresses distributed consensus optimization problems with mixed-integer 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.
Cite
@article{arxiv.2604.14897,
title = {Mix-CALADIN: A Distributed Algorithm for Consensus Mixed-Integer Optimization},
author = {Boyu Han and Xu Du and Karl H. Johansson and Apostolos I. Rikos},
journal= {arXiv preprint arXiv:2604.14897},
year = {2026}
}