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2011
ACM

A quasi-Newton acceleration for high-dimensional optimization algorithms

8 years 5 months ago
A quasi-Newton acceleration for high-dimensional optimization algorithms
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. Keywords Maximum likelihood · Multivariate t · Admixture models · Imaging · Generalized eigenvalues
Hua Zhou, David Alexander, Kenneth Lange
Added 14 May 2011
Updated 14 May 2011
Type Journal
Year 2011
Where SAC
Authors Hua Zhou, David Alexander, Kenneth Lange
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