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Title A quasi-Newton acceleration for high-dimensional optimization algorithms
Edition
Call Number
ISBN/ISSN 0960-3174
Author(s) Hua Zhou
Alexander, David
Lange, Kenneth
Subject(s)
Classification
Series Title Statistics and Computing
GMD Electronic Journal
Language English
Publisher Springer Netherlands
Publishing Year
Publishing Place Netherlands
Collation 13p
Abstract/Notes 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.
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