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arXiv · 1801.00329

ZOOpt: Toolbox for Derivative-Free Optimization

Yu-Ren Liu·Yi-Qi Hu·Hong Qian·Chao Qian·Yang Yu
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Abstract

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.

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Yu-Ren Liu, Yi-Qi Hu, Hong Qian, Chao Qian, Yang Yu. 2017-12-31. ZOOpt: Toolbox for Derivative-Free Optimization. https://doi.org/10.1007/s11432-021-3416-y

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