Optimization on black-box function by parameter-shift rule

Vu Tuan Hai

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arXiv · 2503.13545

Optimization on black-box function by parameter-shift rule

Vu Tuan Hai

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Abstract

Machine learning has been widely applied in many aspects, but training a machine learning model is increasingly difficult. There are more optimization problems named "black-box" where the relationship between model parameters and outcomes is uncertain or complex to trace. Currently, optimizing black-box models that need a large number of query observations and parameters becomes difficult. To overcome the drawbacks of the existing algorithms, in this study, we propose a zeroth-order method that originally came from quantum computing called the parameter-shift rule, which has used a lesser number of parameters than previous methods.

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Vu Tuan Hai. 2025-03-16. Optimization on black-box function by parameter-shift rule. https://arxiv.org/abs/2503.13545

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