Recently, a feasible-side globally convergent modifier-adaptation scheme has been proposed for the real-time optimization of uncertain systems. We show that this scheme is related to proximal-gradient algorithms in numerical optimization and we exploit this relationship to analyze its convergence in the case of inexact gradient information. We also make use of this relationship to propose a novel distributed modifier-adaptation algorithm for interconnected systems that uses a coordinator and knowledge of the interconnection topology. We then prove its feasible-side convergence to plant optimality. In addition, our distributed algorithm ensures confidentiality of local models and data. We finally demonstrate the applicability and effectiveness of this algorithm on a load-sharing optimization case study for serially connected gas compressors. (C) 2018 Elsevier Ltd. All rights reserved.