SIAM Journal on Control and Optimization
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
A Weak-to-Strong Convergence Principle for Fejé-Monotone Methods in Hilbert Spaces
Mathematics of Operations Research
Computers & Mathematics with Applications
SIAM Journal on Scientific Computing
Computational Optimization and Applications
Solving Large-Scale Least Squares Semidefinite Programming by Alternating Direction Methods
SIAM Journal on Matrix Analysis and Applications
Inexact Alternating Direction Methods for Image Recovery
SIAM Journal on Scientific Computing
Alternating Direction Method for Covariance Selection Models
Journal of Scientific Computing
Journal of Computational and Applied Mathematics
Computational Optimization and Applications
An alternating direction method for second-order conic programming
Computers and Operations Research
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This article presents a descent method for solving monotone variational inequalities with separate structures. The descent direction is derived from the well-known alternating directions method. The optimal step size along the descent direction also improves the efficiency of the new method. Some numerical results demonstrate that the new method is effective in practice.