A quasi-Newton acceleration for high-dimensional optimization algorithms |
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Authors: | Hua Zhou David Alexander Kenneth Lange |
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Institution: | (1) Institute of Statistical Science, Academia Sinica, Taipei, 11529, Taiwan;(2) Department of Clinical Laboratory Sciences and Medical Biotechnology, College of Medicine, National Taiwan University, Taipei, Taiwan |
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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. |
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Keywords: | |
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