Cover Image

A Local Polyak-Lojasiewicz and Descent Lemma of Gradient Descent For Overparametrized Linear Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xu, Ziqing, Min, Hancheng, Tarmoun, Salma, Mallada, Enrique, Vidal, Rene
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Convergence Rates for Gradient Descent on the Edge of Stability in Overparametrised Least Squares
by: MacDonald, Lachlan Ewen, et al.
Published: (2025)

On the Convergence of the Gradient Descent Method with Stochastic Fixed-point Rounding Errors under the Polyak-Lojasiewicz Inequality
by: Xia, Lu, et al.
Published: (2023)

Parameter-free Clipped Gradient Descent Meets Polyak
by: Takezawa, Yuki, et al.
Published: (2024)

Dissipative Gradient Descent Ascent Method: A Control Theory Inspired Algorithm for Min-max Optimization
by: Zheng, Tianqi, et al.
Published: (2024)

Gradient Descent with Polyak's Momentum Finds Flatter Minima via Large Catapults
by: Phunyaphibarn, Prin, et al.
Published: (2023)

Neural Collapse under Gradient Flow on Shallow ReLU Networks for Orthogonally Separable Data
by: Min, Hancheng, et al.
Published: (2025)

Scaling Laws for Gradient Descent and Sign Descent for Linear Bigram Models under Zipf's Law
by: Kunstner, Frederik, et al.
Published: (2025)

Understanding the Learning Dynamics of LoRA: A Gradient Flow Perspective on Low-Rank Adaptation in Matrix Factorization
by: Xu, Ziqing, et al.
Published: (2025)

Stochastic Adaptive Gradient Descent Without Descent
by: Aujol, Jean-François, et al.
Published: (2025)

Local Curvature Descent: Squeezing More Curvature out of Standard and Polyak Gradient Descent
by: Richtárik, Peter, et al.
Published: (2024)

Corner Gradient Descent
by: Yarotsky, Dmitry
Published: (2025)

Faster Stochastic Algorithms for Minimax Optimization under Polyak--Łojasiewicz Conditions
by: Chen, Lesi, et al.
Published: (2023)

On the Complexity of Finite-Sum Smooth Optimization under the Polyak-Łojasiewicz Condition
by: Bai, Yunyan, et al.
Published: (2024)

Projective Proximal Gradient Descent for A Class of Nonconvex Nonsmooth Optimization Problems: Fast Convergence Without Kurdyka-Lojasiewicz (KL) Property
by: Yang, Yingzhen, et al.
Published: (2023)

Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification
by: Jung, Hyunji, et al.
Published: (2025)

Gradient Descent Converges Linearly to Flatter Minima than Gradient Flow in Shallow Linear Networks
by: Beneventano, Pierfrancesco, et al.
Published: (2025)

High-Probability Bounds for SGD under the Polyak-Lojasiewicz Condition with Markovian Noise
by: Kar, Avik, et al.
Published: (2026)

Adaptive Conditional Gradient Descent
by: Khademi, Abbas, et al.
Published: (2025)

$k$-SVD with Gradient Descent
by: Jedra, Yassir, et al.
Published: (2025)

Learning Provably Improves the Convergence of Gradient Descent
by: Song, Qingyu, et al.
Published: (2025)

Locally Regularized Sparse Graph by Fast Proximal Gradient Descent
by: Sun, Dongfang, et al.
Published: (2024)

Anytime Acceleration of Gradient Descent
by: Zhang, Zihan, et al.
Published: (2024)

Stochastic Gradient Descent with Strategic Querying
by: Jiang, Nanfei, et al.
Published: (2025)

Stochastic Gradient Descent with Adaptive Data
by: Che, Ethan, et al.
Published: (2024)

Unraveling the Gradient Descent Dynamics of Transformers
by: Song, Bingqing, et al.
Published: (2024)

Enhancing Fractional Gradient Descent with Learned Optimizers
by: Sobotka, Jan, et al.
Published: (2025)

Mirror and Preconditioned Gradient Descent in Wasserstein Space
by: Bonet, Clément, et al.
Published: (2024)

Derivatives of Stochastic Gradient Descent in parametric optimization
by: Iutzeler, Franck, et al.
Published: (2024)

Convergence of Alternating Gradient Descent for Matrix Factorization
by: Ward, Rachel, et al.
Published: (2023)

On Penalty-based Bilevel Gradient Descent Method
by: Shen, Han, et al.
Published: (2023)

Understanding the Implicit Regularization of Gradient Descent in Over-parameterized Models
by: Ma, Jianhao, et al.
Published: (2025)

A Mirror Descent Perspective of Smoothed Sign Descent
by: Wang, Shuyang, et al.
Published: (2024)

Gradient Descent Efficiency Index
by: Dhingra, Aviral
Published: (2024)

Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem
by: Chakrabarti, Kushal, et al.
Published: (2021)

Iterative Pre-Conditioning for Expediting the Gradient-Descent Method: The Distributed Linear Least-Squares Problem
by: Chakrabarti, Kushal, et al.
Published: (2020)

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

On the Convergence of Gradient Descent on Learning Transformers with Residual Connections
by: Qin, Zhen, et al.
Published: (2025)

Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators
by: Li, Tianyou, et al.
Published: (2023)

The Sample Complexity of Gradient Descent in Stochastic Convex Optimization
by: Livni, Roi
Published: (2024)

On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
by: Lin, Tianyi, et al.
Published: (2019)