Panicker, RahulRahulPanickerFaulwasser, TimmTimmFaulwasser2026-10-292026-10-29202627th International Symposium on Mathematical Theory of Networks and Systems MTNS 2026https://hdl.handle.net/11420/65513Dual decomposition is a well-established algorithm to solve strongly convex optimization problems in decentralized fashion. However, as dual decomposition is a first-order method, it often takes a significant number of iterations to converge to acceptable accuracy. We propose a novel dual warm-starting strategy based on learning a predictor of the optimal dual variable of the consensus constraints. We use Gaussian process regression to learn the predictor from pre-computed data. Furthermore, we exploit the posterior variance error bounds of Gaussian Processes and draw upon numerical examples to illustrate the advantages of our approach.en2405-8963202616166Elsevierhttps://creativecommons.org/licenses/by-nc-nd/4.0/Distributed optimizationwarm-startinggaussian processesmachine learningdual decompositionposterior covarianceerror boundNatural Sciences and Mathematics::519: Applied Mathematics, ProbabilitiesComputer Science, Information and General Works::006: Special computer methods::006.3: Artificial Intelligence::006.31: Machine LearningConference Paper10.1016/j.ifacol.2026.09.01110.15480/882.19080