Lightweight Real-Time ALADIN for Distributed Optimization

This paper presents a real-time computational framework for multi-node distributed optimization by extending the Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) algorithm. Our approach integrates adjoint sequential quadratic programming (SQP) techniques to enable efficient approximation of Jacobian information within the ALADIN embedded quadratic program, thereby reducing communication overhead. Furthermore, to decrease computational complexity, we design an event-triggered update strategy that avoids updating Hessian and Jacobian matrices at every iteration. The proposed method achieves local convergence and enhanced communication efficiency, making it well suited for time-critical applications. Numerical experiments demonstrate that our approach achieves competitive performance while exhibiting superior computational efficiency in real-time scenarios, validating its practical applicability for time-sensitive distributed optimization challenges.