-

 

 

 




Optimization Online





 

A Progressive Batching L-BFGS Method for Machine Learning

Raghu Bollapragada(raghu.bollapragada***at***u.northwestern.edu)
Dheevatsa Mudigere(dheevatsa.mudigere***at***intel.com)
Jorge Nocedal(j-nocedal***at***northwestern.edu)
Hao-Jun Michael Shi(hjmshi***at***u.northwestern.edu)
Ping Tak Peter Tang(peter.tang***at***intel.com)

Abstract: The standard L-BFGS method relies on gradient approximations that are not dominated by noise, so that search directions are descent directions, the line search is reliable, and quasi-Newton updating yields useful quadratic models of the objective function. All of this appears to call for a full batch approach, but since small batch sizes give rise to faster algorithms with better generalization properties, L-BFGS is currently not considered an algorithm of choice for large-scale machine learning applications. One need not, however, choose between the two extremes represented by the full batch or highly stochastic regimes, and may instead follow a progressive batching approach in which the sample size increases during the course of the optimization. In this paper, we present a new version of the L-BFGS algorithm that combines three basic components - progressive batching, a stochastic line search, and stable quasi-Newton updating - and that performs well on training logistic regression and deep neural networks. We provide supporting convergence theory for the method.

Keywords: Nonconvex Optimization, Stochastic Optimization, Deep Learning, Sample Selection

Category 1: Nonlinear Optimization

Category 2: Convex and Nonsmooth Optimization (Convex Optimization )

Category 3: Stochastic Programming

Citation:

Download: [PDF]

Entry Submitted: 02/14/2018
Entry Accepted: 02/14/2018
Entry Last Modified: 02/14/2018

Modify/Update this entry


  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository

 

Submit
Update
Policies
Coordinator's Board
Classification Scheme
Credits
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society