Distributed Optimization for Control and Learning. From Theory to Numerical Software Tools


  • Giuseppe Notarstefano
  • Ivano Notarnicola
  • Francesco Farina
  • Andrea Camisa


(to be confirmed by the workshop organisers)

  • Giuseppe Notarstefano, Università di Bologna, Italy
  • Ivano Notarnicola, Università di Bologna, Italy
  • Francesco Farina, Università di Bologna, Italy
  • Andrea Camisa, Università di Bologna, Italy


Cyber-physical network systems give rise to many important control and learning problems in which solving a constrained optimization problem is a fundamental building block. Optimization problems arising in this context are typically large-scale, (i.e., involve a large set of decision variables and/or constraints). Moreover, in many relevant applications these problems are logically and/or spatially distributed in the sense that the computing units have only partial knowledge of the problem. These features call for a novel computation paradigm, termed distributed optimization, in which agents in a network want to cooperatively obtain an optimal solution to the problem by means of local computation and neighboring communication only. This workshop aims to provide an introductory, theoretical foundation for distributed optimization and a set of advanced challenging problems with selected distributed methods. The methodological part will be supported by the numerical implementation of the presented distributed methods using Disropt, a recently developed Python package for distributed optimization.


(to be confirmed by the workshop organisers)

  • Introduction to the workshop
  • Motivating Scenario and Problem Set-ups
  • Introduction to Disropt
  • Coffee Break
  • Distributed Optimization Algorithms for Cost-Coupled Problems
  • Distributed Data Classification with Disropt
  • Lunch Break
  • Constraints Consensus for Common-Cost Problems
  • Distributed Task Assignment with Disropt
  • Coffee Break
  • Distributed Optimization Algorithms for Constraint-Coupled Problems
  • Distributed Optimal Control with Disropt
  • Future Perspectives