tvopt swMATH ID: 36567 Software Authors: Nicola Bastianello Description: tvopt: A Python Framework for Time-Varying Optimization. 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. Homepage: https://arxiv.org/abs/2011.07119 Source Code: https://github.com/nicola-bastianello/tvopt Dependencies: Python Keywords: Mathematical Software; arXiv_cs.MS; Machine Learning; arXiv_cs.LG; Optimization and Control; arXiv_math.OC; Python; Time-Varying Optimization Related Software: NumPy; Python Cited in: 0 Documents Standard Articles 1 Publication describing the Software Year tvopt: A Python Framework for Time-Varying Optimization arXiv Nicola Bastianello 2020