tvopt: A Python Framework for Time-Varying Optimization

TL;DR

tvopt is a Python framework designed for prototyping and benchmarking algorithms for time-varying optimization, supporting centralized and distributed problems with various algorithms and prediction strategies.

Contribution

The paper presents tvopt, a comprehensive Python framework that simplifies development and benchmarking of time-varying optimization algorithms, including new functionalities for prediction and distributed problems.

Findings

Demonstrated effectiveness on benchmark problems

Supported multiple algorithms including gradient and ADMM

Enabled improved online solution accuracy with prediction strategies

Abstract

This paper introduces tvopt, a Python framework for prototyping and benchmarking time-varying (or online) optimization algorithms. The paper first describes the theoretical approach that informed the development of tvopt. Then it discusses the different components of the framework and their use for modeling and solving time-varying optimization problems. In particular, tvopt provides functionalities for defining both centralized and distributed online problems, and a collection of built-in algorithms to solve them, for example gradient-based methods, ADMM and other splitting methods. Moreover, the framework implements prediction strategies to improve the accuracy of the online solvers. The paper then proposes some numerical results on a benchmark problem and discusses their implementation using tvopt. The code for tvopt is available at https://github.com/nicola-bastianello/tvopt.