کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5776071 | 1631961 | 2018 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A novel method for a class of structured low-rank minimizations with equality constraint
ترجمه فارسی عنوان
یک روش جدید برای یک کلاس از کمینه سازی ساختار پایین با محدودیت برابری
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موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات کاربردی
چکیده انگلیسی
The positive semidefinite constraint and equality constraint arise widely in matrix optimization problems of different areas including signal/image processing, finance and risk management. In this paper, an inexact accelerated Augmented Lagrangian Method (ALM) relying on a parameter m is designed to solve the structured low-rank minimization with equality constraint, which is more general and flexible than the existing ALM and its variants. We prove a worst-case O(1âk2) convergence rate of the new method in terms of the residual of the Lagrangian function, and we analyze that when mâ[0,1) the residual of our method is smaller than that of the traditional accelerated ALM. Compared with several state-of-the-art methods, preliminary numerical experiments on solving the Q-weighted low-rank correlation matrix problem from finance validate the efficiency of the proposed method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Computational and Applied Mathematics - Volume 330, 1 March 2018, Pages 475-487
Journal: Journal of Computational and Applied Mathematics - Volume 330, 1 March 2018, Pages 475-487
نویسندگان
Jianchao Bai, Jicheng Li, Fengmin Xu, Pingfan Dai,