ZOOpt: Toolbox for Derivative-Free Optimization

Recent advances in derivative-free optimization allow efficient approximation of the global-optimal solutions of sophisticated functions, such as functions with many local optima, non-differentiable and non-continuous functions. This article describes the ZOOpt (Zeroth Order Optimization) toolbox th...

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Bibliographic Details
Published inarXiv.org
Main Authors Yu-Ren, Liu, Yi-Qi, Hu, Qian, Hong, Chao, Qian, Yang, Yu
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 02.06.2022
Subjects
Artificial intelligence
Computer Science - Learning
Continuity (mathematics)
Machine learning
Optimization
Programming languages
Solvers
Statistics - Machine Learning
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Summary:Recent advances in derivative-free optimization allow efficient approximation of the global-optimal solutions of sophisticated functions, such as functions with many local optima, non-differentiable and non-continuous functions. This article describes the ZOOpt (Zeroth Order Optimization) toolbox that provides efficient derivative-free solvers and is designed easy to use. ZOOpt provides single-machine parallel optimization on the basis of python core and multi-machine distributed optimization for time-consuming tasks by incorporating with the Ray framework -- a famous platform for building distributed applications. ZOOpt particularly focuses on optimization problems in machine learning, addressing high-dimensional and noisy problems such as hyper-parameter tuning and direct policy search. The toolbox is maintained toward a ready-to-use tool in real-world machine learning tasks.
ISSN:2331-8422
DOI:10.48550/arxiv.1801.00329