# A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights

arXiv: https://arxiv.org/abs/1503.01243 · HTML: https://mlsanity.com/paper/1503.01243/

## Key facts

- Cited by 206 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2015.

## Built on by (most cited first, 30 of 206)

- [Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models](https://mlsanity.com/paper/2210.14199/) (2022)
- [Noether's Learning Dynamics: Role of Symmetry Breaking in Neural Networks](https://mlsanity.com/paper/2105.02716/) (2021)
- [Heavy Ball Neural Ordinary Differential Equations](https://mlsanity.com/paper/2110.04840/) (2021)
- [SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality](https://mlsanity.com/paper/2102.04396/) (2021)
- [Continuous vs. Discrete Optimization of Deep Neural Networks](https://mlsanity.com/paper/2107.06608/) (2021)
- [The Confluence of Networks, Games and Learning](https://mlsanity.com/paper/2105.08158/) (2021)
- [Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity](https://mlsanity.com/paper/2202.12482/) (2022)
- [Does Momentum Change the Implicit Regularization on Separable Data?](https://mlsanity.com/paper/2110.03891/) (2021)
- [An SDE for Modeling SAM: Theory and Insights](https://mlsanity.com/paper/2301.08203/) (2023)
- [No-Regret Dynamics in the Fenchel Game: A Unified Framework for Algorithmic Convex Optimization](https://mlsanity.com/paper/2111.11309/) (2021)
- [Noether: The More Things Change, the More Stay the Same](https://mlsanity.com/paper/2104.05508/) (2021)
- [Edge Federated Learning Via Unit-Modulus Over-The-Air Computation](https://mlsanity.com/paper/2101.12051/) (2021)
- [A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip](https://mlsanity.com/paper/2106.07644/) (2021)
- [Discretization Drift in Two-Player Games](https://mlsanity.com/paper/2105.13922/) (2021)
- [Acceleration via Fractal Learning Rate Schedules](https://mlsanity.com/paper/2103.01338/) (2021)
- [On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems](https://mlsanity.com/paper/2107.08225/) (2021)
- [Toward Equation of Motion for Deep Neural Networks: Continuous-time Gradient Descent and Discretization Error Analysis](https://mlsanity.com/paper/2210.15898/) (2022)
- [Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent](https://mlsanity.com/paper/2305.14076/) (2023)
- [Continuous-Time Meta-Learning with Forward Mode Differentiation](https://mlsanity.com/paper/2203.01443/) (2022)
- [Effects of Data Geometry in Early Deep Learning](https://mlsanity.com/paper/2301.00008/) (2022)
- [Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport](https://mlsanity.com/paper/2205.14173/) (2022)
- [Keep the Momentum: Conservation Laws beyond Euclidean Gradient Flows](https://mlsanity.com/paper/2405.12888/) (2024)
- [How Does Momentum Benefit Deep Neural Networks Architecture Design? A Few Case Studies](https://mlsanity.com/paper/2110.07034/) (2021)
- [Convergence Rates of Two-Time-Scale Gradient Descent-Ascent Dynamics for Solving Nonconvex Min-Max Problems](https://mlsanity.com/paper/2112.09579/) (2021)
- [A duality connecting neural network and cosmological dynamics](https://mlsanity.com/paper/2202.11104/) (2022)
- [Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning](https://mlsanity.com/paper/2302.11147/) (2023)
- [Accelerating optimization over the space of probability measures](https://mlsanity.com/paper/2310.04006/) (2023)
- [Momentum Further Constrains Sharpness at the Edge of Stochastic Stability](https://mlsanity.com/paper/2604.14108/) (2026)
- [ANITA: An Optimal Loopless Accelerated Variance-Reduced Gradient Method](https://mlsanity.com/paper/2103.11333/) (2021)
- [On the Hyperparameters in Stochastic Gradient Descent with Momentum](https://mlsanity.com/paper/2108.03947/) (2021)

## Abstract (arXiv)

We derive a second-order ordinary differential equation (ODE) which is the limit of Nesterov's accelerated gradient method. This ODE exhibits approximate equivalence to Nesterov's scheme and thus can serve as a tool for analysis. We show that the continuous time ODE allows for a better understanding of Nesterov's scheme. As a byproduct, we obtain a family of schemes with similar convergence rates. The ODE interpretation also suggests restarting Nesterov's scheme leading to an algorithm, which can be rigorously proven to converge at a linear rate whenever the objective is strongly convex.
