Asymptotic behaviors of semidefinite programming with a covariance perturbation

Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/93902

Title:	Asymptotic behaviors of semidefinite programming with a covariance perturbation
Authors:	Gao, MJ Yiu, KFC
Issue Date:	Oct-2019
Source:	Optimization letters, Oct. 2019, v. 13, no. 7, p. 1631-1649
Abstract:	In this paper, we study asymptotic behaviors of semidefinite programming with a covariance perturbation. We obtain some moderate deviations, Cramér-type moderate deviations and a law of the iterated logarithm of estimates of the respective optimal value and optimal solutions when the covariance matrix is estimated by its sample covariance. As an example, we also apply the main results to the Minimum Trace factor Analysis.
Keywords:	Asymptotic Law of the iterated logarithm Minimum trace factor analysis Moderate deviations Perturbation Semidefinite programming
Publisher:	Springer
Journal:	Optimization letters
ISSN:	1862-4472
EISSN:	1862-4480
DOI:	10.1007/s11590-018-1346-7
Rights:	© Springer-Verlag GmbH Germany, part of Springer Nature 2018 This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s11590-018-1346-7
Appears in Collections:	Journal/Magazine Article

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Status	open access
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