ZOOpt: a toolbox for derivative-free optimization (2017-12-31T00:00:00.000000Z)

TL;DR

The ZOOpt toolbox that provides efficient derivative-free solvers and are designed easy to use, and particularly focuses on optimization problems in machine learning, addressing high-dimensional, noisy, and large-scale problems.

Abstract

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 (this https URL) toolbox that provides efficient derivative-free solvers and are designed easy to use. ZOOpt provides a Python package for single-thread optimization, and a light-weighted distributed version with the help of the Julia language for Python described functions. ZOOpt 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 tool in real-world machine learning tasks.

Authors

Yu-Ren Liu

1 papers

Yi-Qi Hu

1 papers

Hong Qian

1 papers

TL;DR

Abstract

Authors

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Table 4: The mean scores and the standard deviation of the best found policy by each toolbox

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