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Nuclear norm and indicator function model for matrix completion
-
Juan Geng
and
Xiuyu Wang
Published/Copyright:
May 21, 2015
Abstract
In the matrix completion problem, most methods to solve the nuclear norm model are relaxing it to the nuclear norm regularized least squares problem. In this paper, we propose a new unconstraint model for matrix completion problem based on nuclear norm and indicator function and design a proximal point algorithm (PPA-IF) to solve it. Then the convergence of our algorithm is established strictly. Finally, we report numerical results for solving noiseless and noisy matrix completion problems and image reconstruction.
MSC: 90
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 11271367
Received: 2013-12-10
Revised: 2014-6-11
Accepted: 2015-4-5
Published Online: 2015-5-21
Published in Print: 2016-2-1
© 2016 by De Gruyter
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Keywords for this article
Matrix completion;
nuclear norm;
indicator
function;
proximal point algorithm
Articles in the same Issue
- Frontmatter
- Preface
- Lq-regularization for the inverse Robin problem
- Hybrid Newton-type methods for reconstructing sound-soft obstacles from a single far field
- Numerical simulation and parameters inversion for non-symmetric two-sided fractional advection-dispersion equations
- A coupled model of partial differential equations for uranium ores heap leaching and its parameters identification
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- An adaptive multigrid conjugate gradient method for permeability identification of nonlinear diffusion equation
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