Cover Image

Convergence of stochastic gradient descent under a local Lojasiewicz condition for deep neural networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	An, Jing, Lu, Jianfeng
Format:	Preprint
Published:	2023
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 of continuous-time stochastic gradient descent with applications to deep neural networks
by: Lugosi, Gabor, et al.
Published: (2024)

Convergence of two-timescale gradient descent ascent dynamics: finite-dimensional and mean-field perspectives
by: An, Jing, et al.
Published: (2025)

Convergence of gradient descent for deep neural networks
by: Chatterjee, Sourav
Published: (2022)

Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks
by: Jentzen, Arnulf, et al.
Published: (2024)

Convergence of flow-based generative models via proximal gradient descent in Wasserstein space
by: Cheng, Xiuyuan, et al.
Published: (2023)

The duality structure gradient descent algorithm: analysis and applications to neural networks
by: Flynn, Thomas
Published: (2017)

New logarithmic step size for stochastic gradient descent
by: Shamaee, M. Soheil, et al.
Published: (2024)

Convergence of gradient flow for learning convolutional neural networks
by: Diederen, Jona-Maria, et al.
Published: (2026)

A stochastic gradient descent algorithm with random search directions
by: Gbaguidi, Eméric
Published: (2025)

The late-stage training dynamics of (stochastic) subgradient descent on homogeneous neural networks
by: Schechtman, Sholom, 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)

Precise gradient descent training dynamics for finite-width multi-layer neural networks
by: Han, Qiyang, et al.
Published: (2025)

Almost sure convergence rates of stochastic gradient methods under gradient domination
by: Weissmann, Simon, et al.
Published: (2024)

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)

Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance
by: Stollenwerk, Alexander, et al.
Published: (2024)

Learning rate adaptive stochastic gradient descent optimization methods: numerical simulations for deep learning methods for partial differential equations and convergence analyses
by: Dereich, Steffen, et al.
Published: (2024)

Convergence rates of stochastic gradient method with independent sequences of step-size and momentum weight
by: Hwang, Wen-Liang
Published: (2024)

A short proof of near-linear convergence of adaptive gradient descent under fourth-order growth and convexity
by: Davis, Damek, et al.
Published: (2026)

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

Approximation and interpolation of deep neural networks
by: Constantinescu, Vlad-Raul, et al.
Published: (2023)

On the global convergence of gradient descent for wide shallow models with bounded nonlinearities
by: Petit, Romain, et al.
Published: (2026)

On exploration of an interior mirror descent flow for stochastic nonconvex constrained problem
by: Ding, Kuangyu, et al.
Published: (2025)

Convergence analysis of nonmonotone proximal gradient methods under local Lipschitz continuity and Kurdyka--Łojasiewicz property
by: Jia, Xiaoxi, et al.
Published: (2024)

On the stability of gradient descent with second order dynamics for time-varying cost functions
by: Gibson, Travis E., et al.
Published: (2024)

Non-asymptotic convergence analysis of the stochastic gradient Hamiltonian Monte Carlo algorithm with discontinuous stochastic gradient with applications to training of ReLU neural networks
by: Liang, Luxu, et al.
Published: (2024)

Solving a class of stochastic optimal control problems by physics-informed neural networks
by: Jiao, Zhe, et al.
Published: (2024)

Projected gradient methods for nonconvex and stochastic smooth optimization: new complexities and auto-conditioned stepsizes
by: Lan, Guanghui, 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)

Error dynamics of mini-batch gradient descent with random reshuffling for least squares regression
by: Lok, Jackie, et al.
Published: (2024)

Efficient model predictive control for nonlinear systems modelled by deep neural networks
by: Lan, Jianglin
Published: (2024)

Path-conditioned training: a principled way to rescale ReLU neural networks
by: Lebeurrier, Arthur, et al.
Published: (2026)

Non-ergodic linear convergence property of the delayed gradient descent under the strongly convexity and the Polyak-Łojasiewicz condition
by: Choi, Hyung Jun, et al.
Published: (2023)

A stochastic gradient method for trilevel optimization
by: Giovannelli, Tommaso, et al.
Published: (2025)

Generalized EXTRA stochastic gradient Langevin dynamics
by: Gurbuzbalaban, Mert, et al.
Published: (2024)

Global convergence of gradient descent for phase retrieval
by: Fougereux, Théodore, et al.
Published: (2024)

An accelerated first-order regularized momentum descent ascent algorithm for stochastic nonconvex-concave minimax problems
by: Zhang, Huiling, et al.
Published: (2023)

Convergence of projected stochastic natural gradient variational inference for various step size and sample or batch size schedules
by: Guilmeau, Thomas, et al.
Published: (2026)

Recurrent neural networks: vanishing and exploding gradients are not the end of the story
by: Zucchet, Nicolas, et al.
Published: (2024)

Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems
by: Dereich, Steffen, et al.
Published: (2025)