Papers › Fully Adaptive Zeroth-Order Method for Minimizing Functions with Compressible Gradients
Fully Adaptive Zeroth-Order Method for Minimizing Functions with Compressible Gradients
Geovani Nunes Grapiglia, Daniel McKenzie
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
We propose an adaptive zeroth-order method for minimizing differentiable functions with L-Lipschitz continuous gradients. The method is designed to take advantage of the eventual compressibility of the gradient of the objective function, but it does not require knowledge of the approximate sparsity level s or the Lipschitz constant L of the gradient. We show that the new method performs no more than O(n²ϵ⁻²) function evaluations to find an ϵ-approximate stationary point of an objective function with n variables. Assuming additionally that the gradients of the objective function are compressible, we obtain an improved complexity bound of O(slog(n)ϵ⁻²) function evaluations, which holds with high probability. Preliminary numerical results illustrate the efficiency of the proposed method and demonstrate that it can significantly outperform its non-adaptive counterpart.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections