{"ID":2831131,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.08446","arxiv_id":"2512.08446","title":"An Overview of Sensitivity-Based Distributed Optimization and Model Predictive Control","abstract":"This paper presents a concise overview of sensitivity-based methods for solving large-scale optimization problems in distributed fashion. The approach relies on sensitivities and primal decomposition to achieve coordination between the subsystems while requiring only local computations with neighbor-to-neighbor communication. We give a brief historical synopsis of its development and apply it to both static and dynamic optimization problems. Furthermore, a real-time capable distributed model predictive controller is proposed which is experimentally validated on a coupled watertank system.","short_abstract":"This paper presents a concise overview of sensitivity-based methods for solving large-scale optimization problems in distributed fashion. The approach relies on sensitivities and primal decomposition to achieve coordination between the subsystems while requiring only local computations with neighbor-to-neighbor communi...","url_abs":"https://arxiv.org/abs/2512.08446","url_pdf":"https://arxiv.org/pdf/2512.08446v1","authors":"[\"Maximilian Pierer von Esch\",\"Andreas Völz\",\"Knut Graichen\"]","published":"2025-12-09T10:19:56Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}