ZOOpt: Toolbox for Derivative-Free Optimization
This toolbox addresses optimization problems in machine learning, such as hyper-parameter tuning and direct policy search, for researchers and practitioners dealing with high-dimensional and noisy tasks.
The authors introduced ZOOpt, a toolbox for derivative-free optimization that provides efficient solvers for complex functions, including those with many local optima or non-differentiable characteristics, and supports both single-machine parallel and multi-machine distributed optimization via integration with Ray.
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.