A deep learning approach for distributed aggregative optimization with users' feedback

Brumali, Riccardo (2023) A deep learning approach for distributed aggregative optimization with users' feedback. [Laurea magistrale], Università di Bologna, Corso di Studio in Automation engineering / ingegneria dell’automazione [LM-DM270], Documento full-text non disponibile
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This thesis focuses on distributed aggregative optimization, a recently emerged framework where a network of agents cooperate to solve a global optimization problem characterized by local cost functions depending on both the local decision variable and an aggregation of all of them (e.g., the mean of the decision variables of all the agents). The focus is on a so-called personalized version of this scenario in which the local costs consist of a (possibly) time-varying known term and a fixed unknown part, respectively representing the so-called engineering function (concerning measuring quantities such as, e.g., energy or time) and the user’s satisfaction (concerning human preferences whose model cannot be known in advance). In order to compensate for the lack of knowledge about the unknown part of each cost, this work enhances an existing distributed optimization scheme with an automatic differentiation procedure applied to neural networks. More in detail, the designed algorithm combines two independent loops devoted to performing optimization and learning steps. In turn, the distributed optimization algorithm embeds a consensus mechanism aimed at reconstructing in each agent the global information, namely the aggregative variable and the gradient of the cost function with respect to the aggregative variable. Finally, numerical examples involving a quadratic scenario are reported to show the effectiveness of the proposed method comparing already existing algorithms.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Brumali, Riccardo
Relatore della tesi
Correlatore della tesi
Corso di studio
Ordinamento Cds
Parole chiave
online optimization,distributed optimization,deep learning,neural network,users' feedback
Data di discussione della Tesi
14 Ottobre 2023

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