Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Fixed-Point Continuation for $\ell_1$-Minimization: Methodology and Convergence
SIAM Journal on Optimization
An Efficient TVL1 Algorithm for Deblurring Multichannel Images Corrupted by Impulsive Noise
SIAM Journal on Scientific Computing
A Fast Algorithm for Edge-Preserving Variational Multichannel Image Restoration
SIAM Journal on Imaging Sciences
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
SIAM Journal on Imaging Sciences
Exact Matrix Completion via Convex Optimization
Foundations of Computational Mathematics
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
Fixed point and Bregman iterative methods for matrix rank minimization
Mathematical Programming: Series A and B
Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing
SIAM Journal on Scientific Computing
Recovering Low-Rank and Sparse Components of Matrices from Incomplete and Noisy Observations
SIAM Journal on Optimization
Computational Optimization and Applications
Hi-index | 7.29 |
The matrix nuclear norm minimization problem has received much attention in recent years, largely because its highly related to the matrix rank minimization problem arising from controller design, signal processing and model reduction. The alternating direction method is a very popular way to solve this problem due to its simplicity, low storage, practical computation efficiency and nice convergence properties. In this paper, we propose an alternating direction method, where one variable is determined explicitly, and the other variable is computed by a linear conjugate gradient algorithm. At each iteration, the method involves a singular value thresholding and its convergence result is guaranteed in this literature. Extensive experiments illustrate that the proposed algorithm compares favorable with the state-of-the-art algorithms FPCA and IADM_BB which were specifically designed in recent years.