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arXiv · 2202.00865

StepDIRECT -- A Derivative-Free Optimization Method for Stepwise Functions

Dzung T. Phan·Hongsheng Liu·Lam M. Nguyen
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Abstract

In this paper, we propose the StepDIRECT algorithm for derivative-free optimization (DFO), in which the black-box objective function has a stepwise landscape. Our framework is based on the well-known DIRECT algorithm. By incorporating the local variability to explore the flatness, we provide a new criterion to select the potentially optimal hyper-rectangles. In addition, we introduce a stochastic local search algorithm performing on potentially optimal hyper-rectangles to improve the solution quality and convergence speed. Global convergence of the StepDIRECT algorithm is provided. Numerical experiments on optimization for random forest models and hyper-parameter tuning are presented to support the efficacy of our algorithm. The proposed StepDIRECT algorithm shows competitive performance results compared with other state-of-the-art baseline DFO methods including the original DIRECT algorithm.

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BibTeXRIS

Dzung T. Phan, Hongsheng Liu, Lam M. Nguyen. 2022-02-02. StepDIRECT -- A Derivative-Free Optimization Method for Stepwise Functions. https://arxiv.org/abs/2202.00865

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