A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning: Principals, Recent Advances, and Applications

By Sijia Liu, Pin-Yu Chen, Bhavya Kailkhura, Gaoyuan Zhang, A. Hero III, Pramod K. Varshney. IEEE Signal Processing Magazine, 2020

339 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computational Mechanics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/9848db2098c0d3db6ee6f10a177402bc4ec67f83

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Citations in the corpus

Fine-Tuning Language Models with Just Forward Passes (2023, background): “[62] Sijia Liu, Pin-Yu Chen, Bhavya Kailkhura, Gaoyuan Zhang, Alfred O Hero III, and Pramod K Varshney.”