  In this paper we introduce DISROPT, a Python package for distributed
optimization over networks. We focus on cooperative set-ups in which an
optimization problem must be solved by peer-to-peer processors (without central
coordinators) that have access only to partial knowledge of the entire problem.
To reflect this, agents in DISROPT are modeled as entities that are initialized
with their local knowledge of the problem. Agents then run local routines and
communicate with each other to solve the global optimization problem. A simple
syntax has been designed to allow for an easy modeling of the problems. The
package comes with many distributed optimization algorithms that are already
embedded. Moreover, the package provides full-fledged functionalities for
communication and local computation, which can be used to design and implement
new algorithms. DISROPT is available at github.com/disropt/disropt under the
GPL license, with a complete documentation and many examples.
