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

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 eva...

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Vydáno v:IEEE signal processing magazine Ročník 37; číslo 5; s. 43 - 54
Hlavní autoři: Liu, Sijia, Chen, Pin-Yu, Kailkhura, Bhavya, Zhang, Gaoyuan, Hero, Alfred O., Varshney, Pramod K.
Médium: Magazine Article
Jazyk:angličtina
Vydáno: New York IEEE 01.09.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Approximation error
Convergence
Estimation
Linear programming
Machine learning
Optimization
Signal processing
Signal processing algorithms
ISSN:1053-5888, 1558-0792
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Shrnutí: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.
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ISSN:1053-5888
1558-0792
DOI:10.1109/MSP.2020.3003837