2016
JMLR
JMLR 2016
A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights
Abstract
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. [abs] [ pdf ][ bib ] © JMLR 2016. (edit, beta)
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Interdisciplinary Bridge
— Machine Learning and Mathematics & Optimization
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Keyword Pioneer
— accelerated gradient method
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Hot Topic Early Bird
— gradient descent
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Data Science & Analytics, Deep Learning, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning
Authors
Topics
Machine Learning > Optimization & Theory > Neural Network Optimization
Machine Learning > Optimization & Theory > Optimization
Mathematics & Optimization > Optimization > Continuous Optimization
Machine Learning > Core Methods > Optimization
Mathematics & Optimization > Optimization > Convex Optimization