Timezone: »
Poster
SMG: A Shuffling Gradient-Based Method with Momentum
Trang Tran · Lam Nguyen · Quoc Tran-Dinh
We combine two advanced ideas widely used in optimization for machine learning: \textit{shuffling} strategy and \textit{momentum} technique to develop a novel shuffling gradient-based method with momentum, coined \textbf{S}huffling \textbf{M}omentum \textbf{G}radient (SMG), for non-convex finite-sum optimization problems.
While our method is inspired by momentum techniques, its update is fundamentally different from existing momentum-based methods.
We establish state-of-the-art convergence rates of SMG for any shuffling strategy using either constant or diminishing learning rate under standard assumptions (i.e. \textit{$L$-smoothness} and \textit{bounded variance}).
When the shuffling strategy is fixed, we develop another new algorithm that is similar to existing momentum methods,
and prove the same convergence rates for this algorithm under the $L$-smoothness and bounded gradient assumptions.
We demonstrate our algorithms via numerical simulations on standard datasets and compare them with existing shuffling methods.
Our tests have shown encouraging performance of the new algorithms.
Author Information
Trang Tran (Cornell University)
Lam Nguyen (IBM Research, Thomas J. Watson Research Center)
Quoc Tran-Dinh (The University of North Carolina at Chapel Hill)
Related Events (a corresponding poster, oral, or spotlight)
-
2021 Spotlight: SMG: A Shuffling Gradient-Based Method with Momentum »
Wed. Jul 21st 12:25 -- 12:30 PM Room
More from the Same Authors
-
2022 : Fast Convergence for Unstable Reinforcement Learning Problems by Logarithmic Mapping »
Wang Zhang · Lam Nguyen · Subhro Das · Alexandre Megretsky · Luca Daniel · Tsui-Wei Weng -
2023 Poster: ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction »
Wang Zhang · Lily Weng · Subhro Das · Alexandre Megretsky · Luca Daniel · Lam Nguyen -
2022 Poster: Nesterov Accelerated Shuffling Gradient Method for Convex Optimization »
Trang Tran · Katya Scheinberg · Lam Nguyen -
2022 Spotlight: Nesterov Accelerated Shuffling Gradient Method for Convex Optimization »
Trang Tran · Katya Scheinberg · Lam Nguyen -
2020 Poster: Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization »
Quoc Tran-Dinh · Nhan H Pham · Lam Nguyen -
2019 Poster: Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD »
Marten van Dijk · Lam Nguyen · PHUONG_HA NGUYEN · Dzung Phan -
2019 Poster: PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach »
Tsui-Wei Weng · Pin-Yu Chen · Lam Nguyen · Mark Squillante · Akhilan Boopathy · Ivan Oseledets · Luca Daniel -
2019 Oral: Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD »
Marten van Dijk · Lam Nguyen · PHUONG_HA NGUYEN · Dzung Phan -
2019 Oral: PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach »
Tsui-Wei Weng · Pin-Yu Chen · Lam Nguyen · Mark Squillante · Akhilan Boopathy · Ivan Oseledets · Luca Daniel -
2018 Poster: SGD and Hogwild! Convergence Without the Bounded Gradients Assumption »
Lam Nguyen · PHUONG_HA NGUYEN · Marten van Dijk · Peter Richtarik · Katya Scheinberg · Martin Takac -
2018 Oral: SGD and Hogwild! Convergence Without the Bounded Gradients Assumption »
Lam Nguyen · PHUONG_HA NGUYEN · Marten van Dijk · Peter Richtarik · Katya Scheinberg · Martin Takac -
2017 Poster: SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient »
Lam Nguyen · Jie Liu · Katya Scheinberg · Martin Takac -
2017 Talk: SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient »
Lam Nguyen · Jie Liu · Katya Scheinberg · Martin Takac