Cover Image

Perturbed Iterate SGD for Lipschitz Continuous Loss Functions with Numerical Error and Adaptive Step Sizes

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Metel, Michael R.
Format:	Preprint
Published:	2022
Subjects:	Optimization and Control Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Last-Iterate Convergence of Randomized Kaczmarz and SGD with Greedy Step Size
by: Dereziński, Michał, et al.
Published: (2026)

Modified K-means Algorithm with Local Optimality Guarantees
by: Li, Mingyi, et al.
Published: (2025)

AdaBatchGrad: Combining Adaptive Batch Size and Adaptive Step Size
by: Ostroukhov, Petr, et al.
Published: (2024)

Fast Last-Iterate Convergence of SGD in the Smooth Interpolation Regime
by: Attia, Amit, et al.
Published: (2025)

Adaptive Step Sizes for Preconditioned Stochastic Gradient Descent
by: Köhne, Frederik, et al.
Published: (2023)

Randomized Feasibility Methods for Constrained Optimization with Adaptive Step Sizes
by: Chakraborty, Abhishek, et al.
Published: (2026)

A Precise Characterization of SGD Stability Using Loss Surface Geometry
by: Dexter, Gregory, et al.
Published: (2024)

Global Convergence of SGD For Logistic Loss on Two Layer Neural Nets
by: Gopalani, Pulkit, et al.
Published: (2023)

Optimal Projection-Free Adaptive SGD for Matrix Optimization
by: Kovalev, Dmitry
Published: (2026)

SGD with Adaptive Preconditioning: Unified Analysis and Momentum Acceleration
by: Kovalev, Dmitry
Published: (2025)

Byzantine-Robust Distributed SGD: A Unified Analysis and Tight Error Bounds
by: Ruan, Boyuan, et al.
Published: (2026)

Muon Does Not Converge on Convex Lipschitz Functions
by: Parshakova, Tetiana, et al.
Published: (2026)

Shuffling the Data, Stretching the Step-size: Sharper Bias in constant step-size SGD
by: Emmanouilidis, Konstantinos, et al.
Published: (2026)

Convergence of SGD for Training Neural Networks with Sliced Wasserstein Losses
by: Tanguy, Eloi
Published: (2023)

Optimal Growth Schedules for Batch Size and Learning Rate in SGD that Reduce SFO Complexity
by: Umeda, Hikaru, et al.
Published: (2025)

On the Convergence of DP-SGD with Adaptive Clipping
by: Shulgin, Egor, et al.
Published: (2024)

Adaptive SGD with Line-Search and Polyak Stepsizes: Nonconvex Convergence and Accelerated Rates
by: Wu, Haotian
Published: (2025)

Demystifying SGD with Doubly Stochastic Gradients
by: Kim, Kyurae, et al.
Published: (2024)

Stochastic Weakly Convex Optimization Beyond Lipschitz Continuity
by: Gao, Wenzhi, et al.
Published: (2024)

SLowcal-SGD: Slow Query Points Improve Local-SGD for Stochastic Convex Optimization
by: Dahan, Tehila, et al.
Published: (2023)

Revisiting Subgradient Method: Complexity and Convergence Beyond Lipschitz Continuity
by: Li, Xiao, et al.
Published: (2023)

A Convex Loss Function for Set Prediction with Optimal Trade-offs Between Size and Conditional Coverage
by: Bach, Francis
Published: (2025)

Making SGD Parameter-Free
by: Carmon, Yair, et al.
Published: (2022)

On the Trajectories of SGD Without Replacement
by: Beneventano, Pierfrancesco
Published: (2023)

An Efficient Global Optimization Algorithm with Adaptive Estimates of the Local Lipschitz Constants
by: D'Agostino, Danny
Published: (2022)

From PowerSGD to PowerSGD+: Low-Rank Gradient Compression for Distributed Optimization with Convergence Guarantees
by: Xie, Shengping, et al.
Published: (2025)

Shadowheart SGD: Distributed Asynchronous SGD with Optimal Time Complexity Under Arbitrary Computation and Communication Heterogeneity
by: Tyurin, Alexander, et al.
Published: (2024)

Heavy-Tail Phenomenon in Decentralized SGD
by: Gurbuzbalaban, Mert, et al.
Published: (2022)

Dimension-adapted Momentum Outscales SGD
by: Ferbach, Damien, et al.
Published: (2025)

Don't Be So Positive: Negative Step Sizes in Second-Order Methods
by: Shea, Betty, et al.
Published: (2024)

The Rich and the Simple: On the Implicit Bias of Adam and SGD
by: Vasudeva, Bhavya, et al.
Published: (2025)

Sign-SGD via Parameter-Free Optimization
by: Medyakov, Daniil, et al.
Published: (2025)

Can SGD Handle Heavy-Tailed Noise?
by: Fatkhullin, Ilyas, et al.
Published: (2025)

SGD with memory: fundamental properties and stochastic acceleration
by: Yarotsky, Dmitry, et al.
Published: (2024)

Does SGD really happen in tiny subspaces?
by: Song, Minhak, et al.
Published: (2024)

ROOT-SGD: Sharp Nonasymptotics and Near-Optimal Asymptotics in a Single Algorithm
by: Li, Chris Junchi, et al.
Published: (2020)

Gradient Descent on Logistic Regression with Non-Separable Data and Large Step Sizes
by: Meng, Si Yi, et al.
Published: (2024)

Faster Convergence of Local SGD for Over-Parameterized Models
by: Qin, Tiancheng, et al.
Published: (2022)

Global Convergence of SGD On Two Layer Neural Nets
by: Gopalani, Pulkit, et al.
Published: (2022)

Edge of Stochastic Stability: Revisiting the Edge of Stability for SGD
by: Andreyev, Arseniy, et al.
Published: (2024)