title: 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.
authors.affiliation: Department of Human Genetics, University of California, Los Angeles, CA, USA 90095, huazhou@ucla.edu.
authors.forename: Hua
authors.initials: H
authors.lastname: Zhou
authors.forename: David
authors.initials: D
authors.lastname: Alexander
authors.forename: Kenneth
authors.initials: K
authors.lastname: Lange