:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Starnes, Andrew, Webster, Clayton
Format:	Preprint
Published:	2023
Subjects:	Optimization and Control
Online Access:	https://arxiv.org/abs/2311.00531
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Anisotropic Gaussian Smoothing for Gradient-based Optimization
by: Starnes, Andrew, et al.
Published: (2024)

Gaussian smoothing gradient descent for minimizing functions (GSmoothGD)
by: Starnes, Andrew, et al.
Published: (2023)

Examining Policy Entropy of Reinforcement Learning Agents for Personalization Tasks
by: Dereventsov, Anton, et al.
Published: (2022)

Clipped Stochastic Gradient Tracking For Locally Smooth Functions
by: Mei, Leilei, et al.
Published: (2026)

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

Convergence Analysis of Stochastic Accelerated Gradient Methods for Generalized Smooth Optimizations
by: Yu, Chenhao, et al.
Published: (2025)

Stochastic Non-Smooth Convex Optimization with Unbounded Gradients
by: Kovalev, Dmitry
Published: (2026)

Inexactly Smooth Performance Estimation and New Optimized Gradient Methods
by: Zoll, Aaron, et al.
Published: (2026)

Faster Gradient Methods for Highly-Smooth Stochastic Bilevel Optimization
by: Chen, Lesi, et al.
Published: (2025)

Improved Convergence for Decentralized Stochastic Optimization with Biased Gradients
by: Xu, Qing, et al.
Published: (2026)

Adaptive Gradient Normalization and Independent Sampling for (Stochastic) Generalized-Smooth Optimization
by: Yang, Yufeng, et al.
Published: (2024)

Smoothing-Enabled Randomized Stochastic Gradient Schemes for Solving Nonconvex Nonsmooth Potential Games under Uncertainty
by: Xiao, Zhuoyu
Published: (2026)

Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization
by: Shen, Wei, et al.
Published: (2023)

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

Doubly Smoothed Optimistic Gradients: A Universal Approach for Smooth Minimax Problems
by: Zheng, Taoli, et al.
Published: (2025)

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

Inertial Bregman Proximal Gradient under Partial Smoothness
by: Godeme, Jean-Jacques
Published: (2025)

Adaptive Accelerated Gradient Method for Smooth Convex Optimization
by: Wang, Zepeng, et al.
Published: (2025)

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

Nonlinearly Preconditioned Gradient Methods under Generalized Smoothness
by: Oikonomidis, Konstantinos, et al.
Published: (2025)

An Improved Analysis of the Clipped Stochastic subGradient Method under Heavy-Tailed Noise
by: Parletta, Daniela Angela, et al.
Published: (2024)

Dual Averaging Converges for Nonconvex Smooth Stochastic Optimization
by: Liu, Tuo, et al.
Published: (2025)

Decentralized Gradient-Free Methods for Stochastic Non-Smooth Non-Convex Optimization
by: Lin, Zhenwei, et al.
Published: (2023)

A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems
by: Curtis, Frank E., et al.
Published: (2023)

Complexity Lower Bounds of Adaptive Gradient Algorithms for Non-convex Stochastic Optimization under Relaxed Smoothness
by: Crawshaw, Michael, et al.
Published: (2025)

Information Geometry of Exponentiated Gradient: Convergence beyond L-Smoothness
by: Elshiaty, Yara, et al.
Published: (2025)

Revisiting Stochastic Proximal Point Methods: Generalized Smoothness and Similarity
by: Tovmasyan, Zhirayr, et al.
Published: (2025)

Last-Iterate Complexity of SGD for Convex and Smooth Stochastic Problems
by: Garrigos, Guillaume, et al.
Published: (2025)

Variable Smoothing Alternating Proximal Gradient Algorithm for Coupled Composite Optimization
by: Long, Xian-Jun, et al.
Published: (2025)

Smoothing Accelerated Proximal Gradient Method with Backtracking for Nonsmooth Multiobjective Optimization
by: Chengzhi, Huang
Published: (2025)

Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods
by: Vankov, Daniil, et al.
Published: (2024)

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

Trust Region Methods For Nonconvex Stochastic Optimization Beyond Lipschitz Smoothness
by: Xie, Chenghan, et al.
Published: (2023)

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

Directional Smoothness and Gradient Methods: Convergence and Adaptivity
by: Mishkin, Aaron, et al.
Published: (2024)

Gradient-Normalized Smoothness for Optimization with Approximate Hessians
by: Semenov, Andrei, et al.
Published: (2025)

Convergence of the Iterates of the Stochastic Proximal Gradient Method
by: Madariaga, Javier I.
Published: (2026)

Universal Gradient Methods for Stochastic Convex Optimization
by: Rodomanov, Anton, et al.
Published: (2024)

Distributed Adaptive Gradient Algorithm with Gradient Tracking for Stochastic Non-Convex Optimization
by: Han, Dongyu, et al.
Published: (2024)

Stochastic Modified Equations for Stochastic Gradient Descent in Infinite-Dimensional Hilbert Spaces
by: Cerrai, Sandra, et al.
Published: (2026)