DISROPT: a Python Framework for Distributed Optimization

TL;DR

DISROPT is a Python framework that facilitates distributed optimization over networks by enabling peer-to-peer cooperation among agents with partial knowledge, supporting various algorithms and easy problem modeling.

Contribution

It introduces a flexible Python package for distributed optimization with embedded algorithms, communication tools, and a simple syntax for modeling complex problems.

Findings

Provides a comprehensive Python package for distributed optimization.

Supports multiple embedded algorithms and communication functionalities.

Enables easy modeling and implementation of new algorithms.

Abstract

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.