Cover Image

High-Probability Guarantees for Random Zeroth-Order Gradient Descent on Smooth Functions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Ye, Haishan
Format:	Preprint
Published:	2026
Subjects:	Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

High-Probability Guarantees for Random Zeroth-Order (Stochastic) Gradient Descent
by: Ye, Haishan
Published: (2026)

Explicit and Non-asymptotic Query Complexities of Rank-Based Zeroth-order Algorithm on Stochastic Smooth Functions
by: Ye, Haishan
Published: (2025)

A Unified Zeroth-Order Optimization Framework via Oblivious Randomized Sketching
by: Ye, Haishan, et al.
Published: (2025)

Why Does Adaptive Zeroth-Order Optimization Work?
by: Ye, Haishan, et al.
Published: (2026)

On the Inherent Privacy of Zeroth Order Projected Gradient Descent
by: Gupta, Devansh, et al.
Published: (2025)

Can a One-Point Feedback Zeroth-order Algorithm Achieve Linear Dimension Dependent Sample Complexity?
by: Ye, Haishan, et al.
Published: (2025)

Fully Adaptive Zeroth-Order Method for Minimizing Functions with Compressible Gradients
by: Grapiglia, Geovani Nunes, et al.
Published: (2025)

Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient
by: Di, Hao, et al.
Published: (2024)

Robust Analysis of Almost Sure Convergence of Zeroth-Order Mirror Descent Algorithm
by: Paul, Anik Kumar, et al.
Published: (2023)

Gradient Descent for Convex and Smooth Noisy Optimization
by: Hu, Feifei, et al.
Published: (2024)

Zeroth-Order Non-smooth Non-convex Optimization via Gaussian Smoothing
by: Paul, Anik Kumar, et al.
Published: (2025)

Optimal Decentralized Composite Optimization for Convex Functions
by: Ye, Haishan, et al.
Published: (2023)

Minimisation of Polyak-Łojasewicz Functions Using Random Zeroth-Order Oracles
by: Farzin, Amir Ali, et al.
Published: (2024)

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods
by: Jiang, Zhanhong, et al.
Published: (2025)

Fully Zeroth-Order Bilevel Programming via Gaussian Smoothing
by: Aghasi, Alireza, et al.
Published: (2024)

Distributed Zeroth-Order Optimization with Rademacher Perturbations and Momentum Gradient Tracking
by: Su, Yanxu, et al.
Published: (2026)

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise
by: Yang, Yuchen, et al.
Published: (2025)

Generalized Stochastic Gradient Descent with Momentum Methods for Smooth Optimization
by: Wang, Zimeng, et al.
Published: (2026)

Random-Subspace Sequential Quadratic Programming for Constrained Zeroth-Order Optimization
by: Zhang, Runyu, et al.
Published: (2026)

Continuous-Time Zeroth-Order Dynamics with Projection Maps: Model-Free Feedback Optimization with Safety Guarantees
by: Chen, Xin, et al.
Published: (2023)

Zeroth-Order Stochastic Mirror Descent Algorithms for Minimax Excess Risk Optimization
by: Gu, Zhihao, et al.
Published: (2024)

High Probability Convergence Bounds for Non-convex Stochastic Gradient Descent with Sub-Weibull Noise
by: Madden, Liam, et al.
Published: (2020)

A Zeroth-Order Extra-Gradient Method for Black-Box Constrained Optimization
by: Zhou, Yuke, et al.
Published: (2025)

Accelerating Single-Point Zeroth-Order Optimization with Regression-Based Gradient Surrogates
by: Chen, Xin, et al.
Published: (2025)

Stochastic Non-Smooth Non-Convex Optimization with Decision-Dependent Distributions
by: Liu, Chengchang, et al.
Published: (2026)

The Randomized Block Coordinate Descent Method in the Hölder Smooth Setting
by: Maia, Leandro Farias, et al.
Published: (2024)

Toward a Unified Theory of Gradient Descent under Generalized Smoothness
by: Tyurin, Alexander
Published: (2024)

Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity
by: de Vazelhes, William, et al.
Published: (2022)

Variance-Reduced Gradient Estimator for Nonconvex Zeroth-Order Distributed Optimization
by: Mu, Huaiyi, et al.
Published: (2024)

Minimisation of Submodular Functions Using Gaussian Zeroth-Order Random Oracles
by: Farzin, Amir Ali, et al.
Published: (2025)

Using Stochastic Gradient Descent to Smooth Nonconvex Functions: Analysis of Implicit Graduated Optimization
by: Sato, Naoki, et al.
Published: (2023)

Zeroth-Order Non-Convex Optimization for Cooperative Multi-Agent Systems with Diminishing Step Size and Smoothing Radius
by: Zheng, Xinran, et al.
Published: (2024)

Optimal Zeroth-Order Bilevel Optimization
by: Aghasi, Alireza, et al.
Published: (2025)

On the Resolution of Stochastic MPECs over Networks: Distributed Implicit Zeroth-Order Gradient Tracking Methods
by: Ebrahimi, Mohammadjavad, et al.
Published: (2025)

Stochastic Zeroth order Descent with Structured Directions
by: Rando, Marco, et al.
Published: (2022)

Random Coordinate Descent on the Wasserstein Space of Probability Measures
by: Xu, Yewei, et al.
Published: (2026)

Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity
by: Zhou, Qihao, et al.
Published: (2024)

Complexity Guarantees for Zeroth-order Methods via Exponentially-shifted Gaussian Smoothing: Mitigating Dimension-dependence and Incorporating Decision-dependence
by: Wang, Mingrui, et al.
Published: (2026)

Random Function Descent
by: Benning, Felix, et al.
Published: (2023)

Accelerated Gradient Methods with Biased Gradient Estimates: Risk Sensitivity, High-Probability Guarantees, and Large Deviation Bounds
by: Gürbüzbalaban, Mert, et al.
Published: (2025)