Mathematical Programming: Series A and B
A proximal-based decomposition method for convex minimization problems
Mathematical Programming: Series A and B
A variable-penalty alternating directions method for convex optimization
Mathematical Programming: Series A and B
Smoothing Functions for Second-Order-Cone Complementarity Problems
SIAM Journal on Optimization
Proximal Decomposition Via Alternating Linearization
SIAM Journal on Optimization
Computational Optimization and Applications
Interior Point Methods for Second-Order Cone Programming and OR Applications
Computational Optimization and Applications
An unconstrained smooth minimization reformulation of the second-order cone complementarity problem
Mathematical Programming: Series A and B
Solving Second Order Cone Programming via a Reduced Augmented System Approach
SIAM Journal on Optimization
A descent method for structured monotone variational inequalities
Optimization Methods & Software
Regularization Methods for Semidefinite Programming
SIAM Journal on Optimization
An Efficient TVL1 Algorithm for Deblurring Multichannel Images Corrupted by Impulsive Noise
SIAM Journal on Scientific Computing
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
SIAM Journal on Imaging Sciences
IEEE Transactions on Signal Processing
Antenna array pattern synthesis via convex optimization
IEEE Transactions on Signal Processing
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An alternating direction dual augmented Lagrangian method for second-order cone programming (SOCP) problems is proposed. In the algorithm, at each iteration it first minimizes the dual augmented Lagrangian function with respect to the dual variables, and then with respect to the dual slack variables while keeping the other two variables fixed, and then finally it updates the Lagrange multipliers. Convergence result is given. Numerical results demonstrate that our method is fast and efficient, especially for the large-scale second-order cone programming.