swMATH ID: 22396
Software Authors: Yu-Ren Liu, Yi-Qi Hu, Hong Qian, Yang Yu, Chao Qian
Description: ZOOpt/ZOOjl: Toolbox for Derivative-Free Optimization. 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
Homepage: https://github.com/eyounx/ZOOpt
Source Code:  https://github.com/eyounx/ZOOpt
Keywords: Derivative-free optimization; Hyper-parameter optimization; Non-convex optimization; Subset selection; Distributed optimization; arXiv; Learning; arXiv cs.LG; Machine Learning; arXiv stat.ML
Related Software: Python; Auto-WEKA; ZOOjl; ImageProjectiveGeometry.jl; NearestNeighbors.jl; PRMLT; AlexNet; Metalhead.jl; LightNLP.jl; BAMSE; Fizzy; Lingvo; Kpax3; pyGCluster; GmmFlow.jl; EmpiriciSN; LIBSVM.jl; BSMac; Jlnv; GCNv2
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