ZOOpt/ZOOjl: Toolbox for Derivative-Free Optimization

by   Yu-Ren Liu, et al.
Nanjing University

Recent advances of derivative-free optimization allow efficient approximating 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/ZOOjl toolbox that provides efficient derivative-free solvers and are designed easy to use. ZOOpt provides a Python package for single-thread optimization, and ZOOjl provides a distributed version with the help of the Julia language for Python described functions. ZOOpt/ZOOjl toolbox particularly focuses on optimization problems in machine learning, addressing high-dimensional, noisy, and large-scale problems. The toolbox is being maintained toward ready-to-use tools in real-world machine learning tasks.


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