Random zeroth-order gradient descent reaches ε-suboptimal solutions with probability 1-δ using O((dL/μ)log(1/ε) + log(1/δ)) queries deterministically and O(d log(1/ε)(log(1/ε)+log(1/δ))/ε) queries under bounded stochastic noise.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
math.OC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
AdaGrad-Norm last iterate achieves O(1/N^{1/4}) suboptimality for convex non-smooth problems, with tight lower bounds.
citing papers explorer
-
High-Probability Guarantees for Random Zeroth-Order (Stochastic) Gradient Descent
Random zeroth-order gradient descent reaches ε-suboptimal solutions with probability 1-δ using O((dL/μ)log(1/ε) + log(1/δ)) queries deterministically and O(d log(1/ε)(log(1/ε)+log(1/δ))/ε) queries under bounded stochastic noise.
-
Last Iterate Convergence of AdaGrad-Norm for Convex Non-Smooth Optimization
AdaGrad-Norm last iterate achieves O(1/N^{1/4}) suboptimality for convex non-smooth problems, with tight lower bounds.