# A Note on Zeroth-Order Optimization on the Simplex

arXiv: https://arxiv.org/abs/2208.01185 · HTML: https://mlsanity.com/paper/2208.01185/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 2 Aug 2022.

## Builds on (most cited first, 2 of 2)

- [Kernel-based methods for bandit convex optimization](https://mlsanity.com/paper/1607.03084/) (2016)
- [Who Leads and Who Follows in Strategic Classification?](https://mlsanity.com/paper/2106.12529/) (2021)

## 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.