tvopt: A Python Framework for Time-Varying Optimization
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Publication:6353710
arXiv2011.07119MaRDI QIDQ6353710
Author name not available (Why is that?)
Publication date: 12 November 2020
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.
Has companion code repository: https://github.com/nicola-bastianello/optopy
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