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Sensitivity-Based Distributed Programming for Nonconvex Optimization
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DOI：10.1109/tcns.2026.3690575.png)
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Abstract
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This article presents a novel sensitivity-based distributed programming (SBDP) approach for nonconvex large-scale nonlinear programs (NLPs). 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 subsystems is based on primal decomposition. We derive sufficient local convergence conditions for nonconvex 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.
Keywords:
Decomposition
distributed optimal control
distributed optimization
multiagent systems
sensitivities
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