IFAC World Congress 2020 - Workshop
TitleDistributed Optimization for Control and Learning. From Theory to Numerical Software Tools.
Link to the IFAC website of the workshop is here.
Organizers
Giuseppe Notarstefano
Ivano Notarnicola
Francesco Farina
Andrea Camisa
Speakers
Giuseppe Notarstefano
Ivano Notarnicola
Andrea Camisa
Abstract
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.
Schedule (all times are in GMT+2, Berlin)
| 10:00 | Welcome and opening remarks |
| 10:15 | Introduction to distributed optimization |
| 10:45 | Presentation of DISROPT package |
| 11:15 | Coffee break |
| 11:45 | DISROPT: installation and linear average consensus (practical session) |
| 12:15 | Distributed optimization for cost-coupled optimizaiton |
| 13:00 | Lunch break |
| 13:45 | DISROPT: distributed algorithms for machine learning (practical session) |
| 14:45 | Distributed optimization for constraint-coupled optimization |
| 15:30 | DISROPT: distributed primal decomposition for smart grid control (practical session) |
| 16:30 | Concluding remarks |
The practical sessions are designed for unix-like systems (Linux, MacOS, ...) and will be performed on Ubuntu 18.04. In order to keep up with the live session, please make sure that you have a working installation of MPI (e.g. OpenMPI), the Python 3 interpreter (optionally with virtual environment support), a text editor (e.g. gedit).
For instance, to install OpenMPI in Ubuntu 18.04 systems, run the following command on a terminal:
sudo apt-get install openmpi-bin libopenmpi-dev.In MacOS systems, OpenMPI can be installed via homebrew.
Useful links: Link 1. Link 2. Link 3. Link 4. Link 5.
Material
- G. Notarstefano, I. Notarnicola, A. Camisa, “Distributed optimization for smart cyber-physical networks”, Foundations and Trends in Systems and Control, 2019, Download. A free version of the paper is also available on arXiv
- DISROPT Python package on GitHub and PyPI. The technical description can be found here.
