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arXiv · 2604.14897

Mix-CALADIN: A Distributed Algorithm for Consensus Mixed-Integer Optimization

Boyu Han·Xu Du·Karl H. Johansson·Apostolos I. Rikos
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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.

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Boyu Han, Xu Du, Karl H. Johansson, Apostolos I. Rikos. 2026-04-16. Mix-CALADIN: A Distributed Algorithm for Consensus Mixed-Integer Optimization. https://arxiv.org/abs/2604.14897

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