tvopt: A Python Framework for Time-Varying Optimization

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.

arXiv commentCode available here: https://github.com/nicola-bastianello/tvopt -- IEEE CDC'21 paper

References in corpus (6)

• Array Programming with NumPy
• A Class of Prediction-Correction Methods for Time-Varying Convex Optimization
• Prediction-Correction Algorithms for Time-Varying Constrained Optimization
• Asynchronous Distributed Optimization over Lossy Networks via Relaxed ADMM: Stability and Linear Convergence
• Optimization and Learning with Information Streams: Time-varying Algorithms and Applications
• Distributed and Inexact Proximal Gradient Method for Online Convex Optimization

Cited by in corpus (3)

• Internal Model-Based Online Optimization
• A Stochastic Operator Framework for Optimization and Learning with Sub-Weibull Errors
• Distributed Prediction-Correction ADMM for Time-Varying Convex Optimization