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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