论文标题
TVOPT：一个时间变化优化的Python框架
tvopt: A Python Framework for Time-Varying Optimization
论文作者
论文摘要
本文介绍了TVOPT，这是一个用于原型和基准时间变化（或在线）优化算法的Python框架。本文首先描述了理论方法，该方法为TVOPT的发展提供了信息。然后，它讨论了框架的不同组成部分及其用于建模和解决时变优化问题的使用。特别是，TVOPT提供了定义集中式和分布式在线问题的功能，以及内置算法的集合来解决这些问题，例如基于梯度的方法，ADMM和其他拆分方法。此外，该框架实施了预测策略，以提高在线求解器的准确性。然后，本文提出了关于基准问题的一些数值结果，并使用TVOPT讨论了它们的实现。 TVOPT的代码可在https://github.com/nicola-bastianello/tvopt上找到。
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.
