Statistics and Computing

, Volume 21, Issue 2, pp 261–273 | Cite as

A quasi-Newton acceleration for high-dimensional optimization algorithms

Open Access
Article

Abstract

In many statistical problems, maximum likelihood estimation by an EM or MM algorithm suffers from excruciatingly slow convergence. This tendency limits the application of these algorithms to modern high-dimensional problems in data mining, genomics, and imaging. Unfortunately, most existing acceleration techniques are ill-suited to complicated models involving large numbers of parameters. The squared iterative methods (SQUAREM) recently proposed by Varadhan and Roland constitute one notable exception. This paper presents a new quasi-Newton acceleration scheme that requires only modest increments in computation per iteration and overall storage and rivals or surpasses the performance of SQUAREM on several representative test problems.

Maximum likelihood Multivariate t Admixture models Imaging Generalized eigenvalues 

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Copyright information

© The Author(s) 2009

Authors and Affiliations

  1. 1.Department of Human GeneticsUniversity of CaliforniaLos AngelesUSA
  2. 2.Department of BiomathematicsUniversity of CaliforniaLos AngelesUSA
  3. 3.Departments of Biomathematics, Human Genetics, and StatisticsUniversity of CaliforniaLos AngelesUSA

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