 Randomized extrapolation for accelerating EM-type fixed-point algorithmsJournal of Multivariate Analysis, 2023, vol. 196, issue C Abstract: Several extrapolation strategies have been proposed in the literature to accelerate the EM algorithm, with varying degrees of success. One advantage of extrapolation methods is their ease of implementation, as they only require working with the EM iterations and do not need auxiliary quantities, such as gradient and Hessian. In this paper, we introduce a new family of iterative schemes based on vector extrapolation methods. We also construct and numerically test a randomly relaxed version of the scheme. Our results demonstrate that these new strategies can significantly and stably accelerate the convergence of the EM algorithm compared to existing methods. Moreover, these strategies are highly versatile as they can accelerate any linearly convergent fixed point iteration, including EM-type algorithms. Finally, we provide statistical modeling experiments at the end of the paper to demonstrate the applicability and interest of these convergence acceleration schemes, whether applied to the EM algorithm or one of its variants. Keywords: EM algorithm; Randomized extrapolation; Fixed point problems; Convergence acceleration; Data science (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:196:y:2023:i:c:s0047259x23000349 Ordering information: This journal article can be ordered from DOI: 10.1016/j.jmva.2023.105188 Access Statistics for this article Journal of Multivariate Analysis is currently edited by de Leeuw, J. More articles in Journal of Multivariate Analysis from Elsevier |
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