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Understanding the Acceleration Phenomenon via High-Resolution Differential Equations

Bin Shi (bshi001***at***cs.fiu.edu)
Simon Du (ssdu***at***cs.cmu.edu)
Michael Jordan (Correspondence) (jordan***at***cs.berkeley.edu)
Weijie Su (Correspondence) (suw***at***wharton.upenn.edu)

Abstract: Gradient-based optimization algorithms can be studied from the perspective of limiting or- dinary differential equations (ODEs). Motivated by the fact that existing ODEs do not distin- guish between two fundamentally different algorithms—Nesterov’s accelerated gradient method for strongly convex functions (NAG-SC) and Polyak’s heavy-ball method—we study an alter- native limiting process that yields high-resolution ODEs. We show that these ODEs permit a general Lyapunov function framework for the analysis of convergence in both continuous and discrete time. We also show that these ODEs are more accurate surrogates for the underlying algorithms; in particular, they not only distinguish between NAG-SC and Polyak’s heavy-ball method, but they allow the identification of a term that we refer to as “gradient correction” that is present in NAG-SC but not in the heavy-ball method and is responsible for the qualitative difference in convergence of the two methods. We also use the high-resolution ODE framework to study Nesterov’s accelerated gradient method for (non-strongly) convex functions, uncover- ing a hitherto unknown result—that NAG-C minimizes the squared gradient norm at an inverse cubic rate. Finally, by modifying the high-resolution ODE of NAG-C, we obtain a family of new optimization methods that are shown to maintain the accelerated convergence rates of NAG-C for smooth convex functions.

Keywords: Convex optimization, first-order method, Polyak’s heavy ball method, Nesterov’s accelerated gradient methods, ordinary differential equation, Lyapunov function, gradient mini- mization, dimensional analysis, phase space representation, numerical stability

Category 1: Global Optimization (Theory )

Category 2: Convex and Nonsmooth Optimization (Convex Optimization )

Category 3: Nonlinear Optimization (Unconstrained Optimization )

Citation:

Download: [PDF]

Entry Submitted: 10/21/2018
Entry Accepted: 10/22/2018
Entry Last Modified: 12/02/2018

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