Alex Gu, Tsui-Wei Weng, et al.
NeurIPS 2020
Zeroth-order (ZO) optimization is a subset of gradient-free optimization that emerges in many signal processing and machine learning (ML) applications. It is used for solving optimization problems similarly to gradient-based methods. However, it does not require the gradient, using only function evaluations. Specifically, ZO optimization iteratively performs three major steps: gradient estimation, descent direction computation, and the solution update. In this article, we provide a comprehensive review of ZO optimization, with an emphasis on showing the underlying intuition, optimization principles, and recent advances in convergence analysis. Moreover, we demonstrate promising applications of ZO optimization, such as evaluating robustness and generating explanations from black-box deep learning (DL) models and efficient online sensor management.
Alex Gu, Tsui-Wei Weng, et al.
NeurIPS 2020
Pu Zhao, Kaidi Xu, et al.
ASP-DAC 2019
Maurício Gruppi, Sibel Adalı, et al.
NeurIPS 2021
Rulin Shao, Zhouxing Shi, et al.
NeurIPS 2022