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Dissipativity Theory for Nesterov's Accelerated Method

arXiv 1706.04381 ↗First posted 14 Jun 2017math.OCcited by 22

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Abstract (arXiv)

In this paper, we adapt the control theoretic concept of dissipativity theory to provide a natural understanding of Nesterov's accelerated method. Our theory ties rigorous convergence rate analysis to the physically intuitive notion of energy dissipation. Moreover, dissipativity allows one to efficiently construct Lyapunov functions (either numerically or analytically) by solving a small semidefinite program. Using novel supply rate functions, we show how to recover known rate bounds for Nesterov's method and we generalize the approach to certify both linear and sublinear rates in a variety of settings. Finally, we link the continuous-time version of dissipativity to recent works on algorithm analysis that use discretizations of ordinary differential equations.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Jun 2017.

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Citing papers per year: 2021: 8; 2022: 3; 2023: 3; 2024: 4; 2025: 2; 2026: 2.

22 papers.

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  1. 2021No-Regret Dynamics in the Fenchel Game: A Unified Framework for Algorithmic Convex Optimizationcited by 20
  2. 2024DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reductioncited by 10
  3. 2024An Accelerated Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothnesscited by 9
  4. 2021Convergence and Stability of the Stochastic Proximal Point Algorithm with Momentumcited by 7
  5. 2021Revisiting the Role of Euler Numerical Integration on Acceleration and Stability in Convex Optimizationcited by 6
  6. 2021Memory Augmented Optimizers for Deep Learningcited by 5
  7. 2023Continuized Acceleration for Quasar Convex Functions in Non-Convex Optimizationcited by 5
  8. 2024Dissipative Gradient Descent Ascent Method: A Control Theory Inspired Algorithm for Min-max Optimizationcited by 4
  9. 2022Convex Programs and Lyapunov Functions for Reinforcement Learning: A Unified Perspective on the Analysis of Value-Based Methodscited by 3
  10. 2021Transient growth of accelerated optimization algorithmscited by 2
  11. 2021On Centralized and Distributed Mirror Descent: Convergence Analysis Using Quadratic Constraintscited by 2
  12. 2023A Variational Perspective on High-Resolution ODEscited by 2
10 more
  1. 2021Fast symplectic integrator for Nesterov-type acceleration methodcited by 1
  2. 2022Tradeoffs between convergence rate and noise amplification for momentum-based accelerated optimization algorithmscited by 1
  3. 2025Accelerated Gradient Methods with Biased Gradient Estimates: Risk Sensitivity, High-Probability Guarantees, and Large Deviation Boundscited by 1
  4. 2021Online Optimization and Ambiguity-based Learning of Distributionally Uncertain Dynamic Systemscited by 0
  5. 2022Exact Formulas for Finite-Time Estimation Errors of Decentralized Temporal Difference Learning with Linear Function Approximationcited by 0
  6. 2023A Passivity-Based Method for Accelerated Convex Optimisationcited by 0
  7. 2024Generalized Continuous-Time Models for Nesterov's Accelerated Gradient Methodscited by 0
  8. 2025A Concise Lyapunov Analysis of Nesterov's Accelerated Gradient Methodcited by 0
  9. 2026Pseudospectral Bounds for Transient Amplification in Coupled Gradient Descentcited by 0
  10. 2026When Rates Are Geometric: Rate-Certificate Transfer for Contact Splittings in Optimizationcited by 0