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A Note on Zeroth-Order Optimization on the Simplex

Year: 2022

Type: article

Abstract: We construct a zeroth-order gradient estimator for a smooth function defined on the probability simplex. The proposed estimator queries the simplex only. We prove that projected gradient descent and the exponential weights algorithm, when run with this estimator instead of exact gradients, converge at a $\mathcal O(T^{-1/4})$ rate.

Source: CaltechAUTHORS (California Institute of Technology)

DOI: 10.48550/arxiv.2208.01185

Authors: Zrnic, Tijana, Mazumdar, Eric

Institutions: University of California, Berkeley, California Institute of Technology

Language: English

Topic: Stochastic Gradient Optimization Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4289785645

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