Low-Rank Matrix Recovery with Ky Fan 2-k-Norm

Autor:	Stephen A. Vavasis, Xuan Vinh Doan
Přispěvatelé:	Le Thi, H., Le, H., Pham Dinh, T.
Rok vydání:	2019
Předmět:	Control and Optimization Matrix norm Bilinear interpolation Low-rank approximation 010103 numerical & computational mathematics 02 engineering and technology Management Science and Operations Research 01 natural sciences Matrix (mathematics) 0202 electrical engineering electronic engineering information engineering FOS: Mathematics 90C26 65F35 Applied mathematics 0101 mathematics QA Mathematics - Optimization and Control Mathematics Rank minimization Applied Mathematics Convex relaxation Regular polygon Computer Science Applications Proximal point Optimization and Control (math.OC) Norm (mathematics) 020201 artificial intelligence & image processing Convex function
Zdroj:	Advances in Intelligent Systems and Computing ISBN: 9783030218027 WCGO
ISSN:	0925-5001
DOI:	10.1007/978-3-030-21803-4_32
Popis:	We propose Ky Fan 2-k-norm-based models for the nonconvex low-rank matrix recovery problem. A general difference of convex algorithm (DCA) is developed to solve these models. Numerical results show that the proposed models achieve high recoverability rates. Comment: Accepted to WCGO 2019, 6th World Congress on Global Optimization, 8-10 July 2019
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::abe8ead5bd63585bfc2fa0f0597d0c65 https://doi.org/10.1007/978-3-030-21803-4_32 Zobrazit plný text záznamu Full text from SpringerLink