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Open Invited Track

Optimization problems arise as building blocks in several applications of modern cyber-physical network systems. These problems are typically large-scale so that classical optimization algorithms are either inapplicable or do not scale well for problem instances of such a large size. Moreover, in many interesting applications, computing processors have typically a partial knowledge of the problem (e.g. a portion of the cost function or a subset of the constraints) and have to cooperate to compute a global solution of the whole problem.
The objective of this open invited track is to bring together leading researchers working in distributed and large-scale optimization and addressing the main challenges arising in this area. The open invited track focuses on novel algorithmic schemes and analysis tools to enhance the current state of the art in this area and on the application of these methodologies in learning and control problems arising in smart networks. As for theoretical contributions, novel methodologies are sought addressing main challenges arising in distributed optimization both in terms of network communication and in terms of problem set-up, including novel contributions in addressing the considered problems via system theoretic analysis tools. As for the applications, proposed contributions will show how distributed optimization algorithms can be applied to relevant learning and control problems in smart networks, as, e.g., smart grids and robotic networks.
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