Lightweight Real-Time ALADIN for Distributed Optimization

TL;DR

This paper introduces a real-time distributed optimization framework extending ALADIN, incorporating adjoint SQP and event-triggered updates to improve efficiency and convergence in time-critical applications.

Contribution

It develops a novel real-time ALADIN-based method with Jacobian approximation and event-triggered updates, reducing communication and computational costs.

Findings

Achieves local convergence with improved communication efficiency.

Demonstrates competitive performance in real-time distributed optimization.

Validates practical applicability through numerical experiments.

Abstract

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.