A Note on Zeroth-Order Optimization on the Simplex
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 2 of its 9 references that are in the map. First posted on arXiv 2 Aug 2022.
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References · 2
- 2016Kernel-based methods for bandit convex optimizationcited by 63
- 2021Who Leads and Who Follows in Strategic Classification?cited by 53
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