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

arXiv: https://arxiv.org/abs/2202.00865 · HTML: https://mlsanity.com/paper/2202.00865/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2022.

## Built on by (most cited first, 1 of 1)

- [Reinforcement Learning Guided Multi-Objective Exam Paper Generation](https://mlsanity.com/paper/2303.01042/) (2023)

## Abstract (arXiv)

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.