Sensitivity-Based Distributed Programming for Non-Convex Optimization
Abstract
This paper presents a novel sensitivity-based distributed programming (SBDP) approach for non-convex, large-scale nonlinear programs (NLP). The algorithm relies on first-order sensitivities to cooperatively solve the central NLP in a distributed manner with only neighbor-to-neighbor communication and parallelizable local computations. The decoupling of the subsystems is based on primal decomposition. We derive sufficient local convergence conditions for non-convex problems. Furthermore, we consider the SBDP method in a distributed optimal control context and derive favorable convergence properties in this setting. We illustrate these theoretical findings and the performance of the proposed method with a comparison to state-of-the-art algorithms and simulations of various distributed optimization and control problems.
Cite
@article{arxiv.2503.10174,
title = {Sensitivity-Based Distributed Programming for Non-Convex Optimization},
author = {Maximilian Pierer von Esch and Andreas Völz and Knut Graichen},
journal= {arXiv preprint arXiv:2503.10174},
year = {2026}
}