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Convergence of a Stochastic Gradient Method with Momentum for Non-Smooth
  Non-Convex Optimization
v1v2 (latest)

Convergence of a Stochastic Gradient Method with Momentum for Non-Smooth Non-Convex Optimization

International Conference on Machine Learning (ICML), 2020
13 February 2020
Vien V. Mai
M. Johansson
ArXiv (abs)PDFHTML

Papers citing "Convergence of a Stochastic Gradient Method with Momentum for Non-Smooth Non-Convex Optimization"

37 / 37 papers shown
Adaptive Memory Momentum via a Model-Based Framework for Deep Learning Optimization
Adaptive Memory Momentum via a Model-Based Framework for Deep Learning Optimization
Kristi Topollai
A. Choromańska
ODL
415
1
0
06 Oct 2025
Accelerating SGDM via Learning Rate and Batch Size Schedules: A Lyapunov-Based Analysis
Accelerating SGDM via Learning Rate and Batch Size Schedules: A Lyapunov-Based Analysis
Yuichi Kondo
Hideaki Iiduka
118
0
0
05 Aug 2025
HOME-3: High-Order Momentum Estimator with Third-Power Gradient for Convex and Smooth Nonconvex Optimization
HOME-3: High-Order Momentum Estimator with Third-Power Gradient for Convex and Smooth Nonconvex Optimization
Wei Zhang
Arif Hassan Zidan
Arif Hassan Zidan
Wei Zhang
Tianming Liu
ODL
302
0
0
16 May 2025
Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum
Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum
Yuan Zhou
Xinli Shi
Xuelong Li
Jiachen Zhong
G. Wen
Jinde Cao
FedML
418
3
0
17 Apr 2025
Increasing Batch Size Improves Convergence of Stochastic Gradient Descent with Momentum
Increasing Batch Size Improves Convergence of Stochastic Gradient Descent with Momentum
Keisuke Kamo
Hideaki Iiduka
431
3
0
15 Jan 2025
On the Performance Analysis of Momentum Method: A Frequency Domain Perspective
On the Performance Analysis of Momentum Method: A Frequency Domain PerspectiveInternational Conference on Learning Representations (ICLR), 2024
Xianliang Li
Jun Luo
Zhiwei Zheng
Hanxiao Wang
Li Luo
Lingkun Wen
Linlong Wu
Sheng Xu
660
4
0
29 Nov 2024
Reevaluating Theoretical Analysis Methods for Optimization in Deep Learning
Reevaluating Theoretical Analysis Methods for Optimization in Deep Learning
Hoang Tran
Qinzi Zhang
Ashok Cutkosky
430
4
0
01 Jul 2024
Towards Exact Gradient-based Training on Analog In-memory Computing
Towards Exact Gradient-based Training on Analog In-memory ComputingNeural Information Processing Systems (NeurIPS), 2024
Zhaoxian Wu
Tayfun Gokmen
Malte J. Rasch
Tianyi Chen
411
7
0
18 Jun 2024
Almost sure convergence rates of stochastic gradient methods under gradient domination
Almost sure convergence rates of stochastic gradient methods under gradient domination
Simon Weissmann
Sara Klein
Waïss Azizian
Leif Döring
435
7
0
22 May 2024
Random Scaling and Momentum for Non-smooth Non-convex Optimization
Random Scaling and Momentum for Non-smooth Non-convex OptimizationInternational Conference on Machine Learning (ICML), 2024
Qinzi Zhang
Ashok Cutkosky
330
9
0
16 May 2024
Leveraging Continuous Time to Understand Momentum When Training Diagonal
  Linear Networks
Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear NetworksInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Hristo Papazov
Scott Pesme
Nicolas Flammarion
375
10
0
08 Mar 2024
Stochastic Weakly Convex Optimization Beyond Lipschitz Continuity
Stochastic Weakly Convex Optimization Beyond Lipschitz ContinuityInternational Conference on Machine Learning (ICML), 2024
Wenzhi Gao
Qi Deng
307
6
0
25 Jan 2024
Accelerated Convergence of Stochastic Heavy Ball Method under
  Anisotropic Gradient Noise
Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise
Boyao Wang
Yuxing Liu
Xiaoyu Wang
Tong Zhang
283
8
0
22 Dec 2023
High Probability Convergence of Adam Under Unbounded Gradients and
  Affine Variance Noise
High Probability Convergence of Adam Under Unbounded Gradients and Affine Variance Noise
Yusu Hong
Junhong Lin
278
11
0
03 Nov 2023
Acceleration of stochastic gradient descent with momentum by averaging:
  finite-sample rates and asymptotic normality
Acceleration of stochastic gradient descent with momentum by averaging: finite-sample rates and asymptotic normality
Kejie Tang
Weidong Liu
Yichen Zhang
Xi Chen
245
4
0
28 May 2023
A Unified Momentum-based Paradigm of Decentralized SGD for Non-Convex
  Models and Heterogeneous Data
A Unified Momentum-based Paradigm of Decentralized SGD for Non-Convex Models and Heterogeneous Data
Haizhou Du
Chengdong Ni
183
3
0
01 Mar 2023
Delayed Stochastic Algorithms for Distributed Weakly Convex Optimization
Delayed Stochastic Algorithms for Distributed Weakly Convex Optimization
W. Gao
Qinhao Deng
373
0
0
30 Jan 2023
Policy Gradient in Robust MDPs with Global Convergence Guarantee
Policy Gradient in Robust MDPs with Global Convergence GuaranteeInternational Conference on Machine Learning (ICML), 2022
Qiuhao Wang
C. Ho
Marek Petrik
506
40
0
20 Dec 2022
Towards understanding how momentum improves generalization in deep
  learning
Towards understanding how momentum improves generalization in deep learningInternational Conference on Machine Learning (ICML), 2022
Samy Jelassi
Yuanzhi Li
ODLMLTAI4CE
231
53
0
13 Jul 2022
Convergence of First-Order Methods for Constrained Nonconvex
  Optimization with Dependent Data
Convergence of First-Order Methods for Constrained Nonconvex Optimization with Dependent DataInternational Conference on Machine Learning (ICML), 2022
Ahmet Alacaoglu
Hanbaek Lyu
369
6
0
29 Mar 2022
Personalized incentives as feedback design in generalized Nash
  equilibrium problems
Personalized incentives as feedback design in generalized Nash equilibrium problemsIEEE Transactions on Automatic Control (TAC), 2022
F. Fabiani
Andrea Simonetto
Paul Goulart
326
8
0
24 Mar 2022
On Almost Sure Convergence Rates of Stochastic Gradient Methods
On Almost Sure Convergence Rates of Stochastic Gradient MethodsAnnual Conference Computational Learning Theory (COLT), 2022
Jun Liu
Ye Yuan
310
54
0
09 Feb 2022
On the Convergence of mSGD and AdaGrad for Stochastic Optimization
On the Convergence of mSGD and AdaGrad for Stochastic OptimizationInternational Conference on Learning Representations (ICLR), 2022
Ruinan Jin
Yu Xing
Xingkang He
176
12
0
26 Jan 2022
Convergence of an Asynchronous Block-Coordinate Forward-Backward
  Algorithm for Convex Composite Optimization
Convergence of an Asynchronous Block-Coordinate Forward-Backward Algorithm for Convex Composite OptimizationComputational optimization and applications (Comput. Optim. Appl.), 2022
Cheik Traoré
Saverio Salzo
S. Villa
270
2
0
14 Jan 2022
Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis
Non-convex Distributionally Robust Optimization: Non-asymptotic AnalysisNeural Information Processing Systems (NeurIPS), 2021
Jikai Jin
Samir Bhatt
Haiyang Wang
Liwei Wang
452
59
0
24 Oct 2021
Distributed stochastic inertial-accelerated methods with delayed
  derivatives for nonconvex problems
Distributed stochastic inertial-accelerated methods with delayed derivatives for nonconvex problemsSIAM Journal of Imaging Sciences (SIAM J. Imaging Sci.), 2021
Yangyang Xu
Yibo Xu
Yonggui Yan
Jiewei Chen
347
5
0
24 Jul 2021
Robust Regression via Model Based Methods
Robust Regression via Model Based Methods
Armin Moharrer
Khashayar Kamran
E. Yeh
Stratis Ioannidis
343
0
0
20 Jun 2021
Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex
  Optimization
Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex OptimizationNeural Information Processing Systems (NeurIPS), 2021
Qi Deng
Wenzhi Gao
319
17
0
06 Jun 2021
Bandwidth-based Step-Sizes for Non-Convex Stochastic Optimization
Bandwidth-based Step-Sizes for Non-Convex Stochastic Optimization
Xiaoyu Wang
M. Johansson
293
2
0
05 Jun 2021
OpReg-Boost: Learning to Accelerate Online Algorithms with Operator
  Regression
OpReg-Boost: Learning to Accelerate Online Algorithms with Operator RegressionConference on Learning for Dynamics & Control (L4DC), 2021
Nicola Bastianello
Andrea Simonetto
E. Dall’Anese
446
3
0
27 May 2021
Scale Invariant Monte Carlo under Linear Function Approximation with
  Curvature based step-size
Scale Invariant Monte Carlo under Linear Function Approximation with Curvature based step-size
Rahul Madhavan
Hemant Makwana
209
0
0
15 Apr 2021
Stability and Convergence of Stochastic Gradient Clipping: Beyond
  Lipschitz Continuity and Smoothness
Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and SmoothnessInternational Conference on Machine Learning (ICML), 2021
Vien V. Mai
M. Johansson
331
51
0
12 Feb 2021
Minibatch optimal transport distances; analysis and applications
Minibatch optimal transport distances; analysis and applications
Kilian Fatras
Younes Zine
Szymon Majewski
Rémi Flamary
Rémi Gribonval
Nicolas Courty
OT
300
73
0
05 Jan 2021
Stochastic optimization with momentum: convergence, fluctuations, and
  traps avoidance
Stochastic optimization with momentum: convergence, fluctuations, and traps avoidance
Anas Barakat
Pascal Bianchi
W. Hachem
S. Schechtman
360
16
0
07 Dec 2020
A Modular Analysis of Provable Acceleration via Polyak's Momentum:
  Training a Wide ReLU Network and a Deep Linear Network
A Modular Analysis of Provable Acceleration via Polyak's Momentum: Training a Wide ReLU Network and a Deep Linear NetworkInternational Conference on Machine Learning (ICML), 2020
Jun-Kun Wang
Chi-Heng Lin
Jacob D. Abernethy
726
26
0
04 Oct 2020
Momentum via Primal Averaging: Theoretical Insights and Learning Rate
  Schedules for Non-Convex Optimization
Momentum via Primal Averaging: Theoretical Insights and Learning Rate Schedules for Non-Convex Optimization
Aaron Defazio
344
29
0
01 Oct 2020
Convergence of adaptive algorithms for weakly convex constrained
  optimization
Convergence of adaptive algorithms for weakly convex constrained optimization
Ahmet Alacaoglu
Yura Malitsky
Volkan Cevher
241
14
0
11 Jun 2020
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