From PowerSGD to PowerSGD+: Low-Rank Gradient Compression for Distributed Optimization with Convergence Guarantees
Fuente:
arXiv
Saved in:
| Main Authors: | Xie, Shengping, Chen, Chuyan, Yuan, Kun |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Greedy Low-Rank Gradient Compression for Distributed Learning with Convergence Guarantees
by: Chen, Chuyan, et al.
Published: (2025)
by: Chen, Chuyan, et al.
Published: (2025)
Subspace Optimization for Large Language Models with Convergence Guarantees
by: He, Yutong, et al.
Published: (2024)
by: He, Yutong, et al.
Published: (2024)
High-Probability Convergence Guarantees of Decentralized SGD
by: Armacki, Aleksandar, et al.
Published: (2025)
by: Armacki, Aleksandar, et al.
Published: (2025)
From Gradient Clipping to Normalization for Heavy Tailed SGD
by: Hübler, Florian, et al.
Published: (2024)
by: Hübler, Florian, et al.
Published: (2024)
Heavy-Tail Phenomenon in Decentralized SGD
by: Gurbuzbalaban, Mert, et al.
Published: (2022)
by: Gurbuzbalaban, Mert, et al.
Published: (2022)
Demystifying SGD with Doubly Stochastic Gradients
by: Kim, Kyurae, et al.
Published: (2024)
by: Kim, Kyurae, et al.
Published: (2024)
Revisiting Gradient Normalization and Clipping for Nonconvex SGD under Heavy-Tailed Noise: Necessity, Sufficiency, and Acceleration
by: Sun, Tao, et al.
Published: (2024)
by: Sun, Tao, et al.
Published: (2024)
SLowcal-SGD: Slow Query Points Improve Local-SGD for Stochastic Convex Optimization
by: Dahan, Tehila, et al.
Published: (2023)
by: Dahan, Tehila, et al.
Published: (2023)
Faster Convergence of Local SGD for Over-Parameterized Models
by: Qin, Tiancheng, et al.
Published: (2022)
by: Qin, Tiancheng, et al.
Published: (2022)
Global Convergence of SGD On Two Layer Neural Nets
by: Gopalani, Pulkit, et al.
Published: (2022)
by: Gopalani, Pulkit, et al.
Published: (2022)
Diagonalisation SGD: Fast & Convergent SGD for Non-Differentiable Models via Reparameterisation and Smoothing
by: Wagner, Dominik, et al.
Published: (2024)
by: Wagner, Dominik, et al.
Published: (2024)
VAMO: Efficient Zeroth-Order Variance Reduction for SGD with Faster Convergence
by: Chen, Jiahe, et al.
Published: (2025)
by: Chen, Jiahe, et al.
Published: (2025)
On the Convergence of DP-SGD with Adaptive Clipping
by: Shulgin, Egor, et al.
Published: (2024)
by: Shulgin, Egor, et al.
Published: (2024)
Shadowheart SGD: Distributed Asynchronous SGD with Optimal Time Complexity Under Arbitrary Computation and Communication Heterogeneity
by: Tyurin, Alexander, et al.
Published: (2024)
by: Tyurin, Alexander, et al.
Published: (2024)
Fast Last-Iterate Convergence of SGD in the Smooth Interpolation Regime
by: Attia, Amit, et al.
Published: (2025)
by: Attia, Amit, et al.
Published: (2025)
The Optimality of (Accelerated) SGD for High-Dimensional Quadratic Optimization
by: Zhang, Haihan, et al.
Published: (2024)
by: Zhang, Haihan, et al.
Published: (2024)
Sign-SGD via Parameter-Free Optimization
by: Medyakov, Daniil, et al.
Published: (2025)
by: Medyakov, Daniil, et al.
Published: (2025)
A Comprehensive Framework for Analyzing the Convergence of Adam: Bridging the Gap with SGD
by: Jin, Ruinan, et al.
Published: (2024)
by: Jin, Ruinan, et al.
Published: (2024)
Global Convergence of SGD For Logistic Loss on Two Layer Neural Nets
by: Gopalani, Pulkit, et al.
Published: (2023)
by: Gopalani, Pulkit, et al.
Published: (2023)
Convergence of SGD for Training Neural Networks with Sliced Wasserstein Losses
by: Tanguy, Eloi
Published: (2023)
by: Tanguy, Eloi
Published: (2023)
SGD with Partial Hessian for Deep Neural Networks Optimization
by: Sun, Ying, et al.
Published: (2024)
by: Sun, Ying, et al.
Published: (2024)
Optimal Projection-Free Adaptive SGD for Matrix Optimization
by: Kovalev, Dmitry
Published: (2026)
by: Kovalev, Dmitry
Published: (2026)
On the Provable Suboptimality of Momentum SGD in Nonstationary Stochastic Optimization
by: Sahu, Sharan, et al.
Published: (2026)
by: Sahu, Sharan, et al.
Published: (2026)
Convergence of Clipped-SGD for Convex $(L_0,L_1)$-Smooth Optimization with Heavy-Tailed Noise
by: Chezhegov, Savelii, et al.
Published: (2025)
by: Chezhegov, Savelii, et al.
Published: (2025)
Making SGD Parameter-Free
by: Carmon, Yair, et al.
Published: (2022)
by: Carmon, Yair, et al.
Published: (2022)
On the Trajectories of SGD Without Replacement
by: Beneventano, Pierfrancesco
Published: (2023)
by: Beneventano, Pierfrancesco
Published: (2023)
Differentially Private Clipped-SGD: High-Probability Convergence with Arbitrary Clipping Level
by: Khah, Saleh Vatan, et al.
Published: (2025)
by: Khah, Saleh Vatan, et al.
Published: (2025)
Adaptive SGD with Line-Search and Polyak Stepsizes: Nonconvex Convergence and Accelerated Rates
by: Wu, Haotian
Published: (2025)
by: Wu, Haotian
Published: (2025)
Convergence of SGD with momentum in the nonconvex case: A time window-based analysis
by: Qiu, Junwen, et al.
Published: (2024)
by: Qiu, Junwen, et al.
Published: (2024)
Convergence and concentration properties of constant step-size SGD through Markov chains
by: Merad, Ibrahim, et al.
Published: (2023)
by: Merad, Ibrahim, et al.
Published: (2023)
A Minibatch-SGD-Based Learning Meta-Policy for Inventory Systems with Myopic Optimal Policy
by: Lyu, Jiameng, et al.
Published: (2024)
by: Lyu, Jiameng, et al.
Published: (2024)
Dimension-adapted Momentum Outscales SGD
by: Ferbach, Damien, et al.
Published: (2025)
by: Ferbach, Damien, et al.
Published: (2025)
AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent
by: Surjanovic, Nikola, et al.
Published: (2025)
by: Surjanovic, Nikola, et al.
Published: (2025)
Accelerating Single-Pass SGD for Generalized Linear Prediction
by: Chen, Qian, et al.
Published: (2026)
by: Chen, Qian, et al.
Published: (2026)
Understanding Outer Optimizers in Local SGD: Learning Rates, Momentum, and Acceleration
by: Khaled, Ahmed, et al.
Published: (2025)
by: Khaled, Ahmed, et al.
Published: (2025)
Non-Euclidean SGD for Structured Optimization: Unified Analysis and Improved Rates
by: Kovalev, Dmitry, et al.
Published: (2025)
by: Kovalev, Dmitry, et al.
Published: (2025)
SketchySGD: Reliable Stochastic Optimization via Randomized Curvature Estimates
by: Frangella, Zachary, et al.
Published: (2022)
by: Frangella, Zachary, et al.
Published: (2022)
Proactive DP: A Multple Target Optimization Framework for DP-SGD
by: van Dijk, Marten, et al.
Published: (2021)
by: van Dijk, Marten, et al.
Published: (2021)
Scaling Laws of SignSGD in Linear Regression: When Does It Outperform SGD?
by: Kim, Jihwan, et al.
Published: (2026)
by: Kim, Jihwan, et al.
Published: (2026)
Byzantine-Robust Distributed SGD: A Unified Analysis and Tight Error Bounds
by: Ruan, Boyuan, et al.
Published: (2026)
by: Ruan, Boyuan, et al.
Published: (2026)
Similar Items
-
Greedy Low-Rank Gradient Compression for Distributed Learning with Convergence Guarantees
by: Chen, Chuyan, et al.
Published: (2025) -
Subspace Optimization for Large Language Models with Convergence Guarantees
by: He, Yutong, et al.
Published: (2024) -
High-Probability Convergence Guarantees of Decentralized SGD
by: Armacki, Aleksandar, et al.
Published: (2025) -
From Gradient Clipping to Normalization for Heavy Tailed SGD
by: Hübler, Florian, et al.
Published: (2024) -
Heavy-Tail Phenomenon in Decentralized SGD
by: Gurbuzbalaban, Mert, et al.
Published: (2022)