Sensitivity-Based Distributed Programming for Nonconvex 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.
Fields of Study & Subjects
Distributed Control Multi-Agent Systems
Stochastic Gradient Optimization Techniques
Optimization and Variational Analysis
Publication details
- Journal / source
- IEEE Transactions on Control of Network Systems
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Volume
- 13
- Issue
- 2
- Pages
- 1205–1217
- Publication date
- 5 May 2026
- Language
- EN