Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Mathematical Programming: Series A and B
Interior Gradient and Proximal Methods for Convex and Conic Optimization
SIAM Journal on Optimization
Approximation accuracy, gradient methods, and error bound for structured convex optimization
Mathematical Programming: Series A and B - 20th International Symposium on Mathematical Programming – ISMP 2009
Mathematical Programming: Series A and B
SIAM Journal on Imaging Sciences
Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing
SIAM Journal on Scientific Computing
Alternating Direction Method for Covariance Selection Models
Journal of Scientific Computing
IEEE Transactions on Image Processing
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In this paper, we study the alternating direction method for finding the Dantzig selectors, which are first introduced in Candes and Tao (2007a). In particular, at each iteration we apply the nonmonotone gradient method proposed in Lu and Zhang (in press) to approximately solve one subproblem of this method. We compare our approach with a first-order method proposed in Becker et al. (2011). The computational results show that our approach usually outperforms that method in terms of CPU time while producing solutions of comparable quality.