Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
A proximal-based decomposition method for convex minimization problems
Mathematical Programming: Series A and B
Matrix computations (3rd ed.)
A variable-penalty alternating directions method for convex optimization
Mathematical Programming: Series A and B
Alternating Projection-Proximal Methods for Convex Programming and Variational Inequalities
SIAM Journal on Optimization
A Dual Approach to Semidefinite Least-Squares Problems
SIAM Journal on Matrix Analysis and Applications
Least-Squares Covariance Matrix Adjustment
SIAM Journal on Matrix Analysis and Applications
Solving Lift-and-Project Relaxations of Binary Integer Programs
SIAM Journal on Optimization
A Quadratically Convergent Newton Method for Computing the Nearest Correlation Matrix
SIAM Journal on Matrix Analysis and Applications
Implementation of a primal—dual method for SDP on a shared memory parallel architecture
Computational Optimization and Applications
An inexact primal–dual path following algorithm for convex quadratic SDP
Mathematical Programming: Series A and B
Matrix Nearness Problems with Bregman Divergences
SIAM Journal on Matrix Analysis and Applications
The rate of convergence of the augmented Lagrangian method for nonlinear semidefinite programming
Mathematical Programming: Series A and B
A descent method for structured monotone variational inequalities
Optimization Methods & Software
Regularization Methods for Semidefinite Programming
SIAM Journal on Optimization
Calibrating Least Squares Semidefinite Programming with Equality and Inequality Constraints
SIAM Journal on Matrix Analysis and Applications
A Newton-CG Augmented Lagrangian Method for Semidefinite Programming
SIAM Journal on Optimization
SIAM Journal on Scientific Computing
Recovering Low-Rank and Sparse Components of Matrices from Incomplete and Noisy Observations
SIAM Journal on Optimization
Inexact Alternating Direction Methods for Image Recovery
SIAM Journal on Scientific Computing
On the $O(1/n)$ Convergence Rate of the Douglas-Rachford Alternating Direction Method
SIAM Journal on Numerical Analysis
Journal of Computational and Applied Mathematics
An ADM-based splitting method for separable convex programming
Computational Optimization and Applications
Advances in Computational Mathematics
A proximal parallel splitting method for minimizing sum of convex functions with linear constraints
Journal of Computational and Applied Mathematics
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The well-known least squares semidefinite programming (LSSDP) problem seeks the nearest adjustment of a given symmetric matrix in the intersection of the cone of positive semidefinite matrices and a set of linear constraints, and it captures many applications in diversing fields. The task of solving large-scale LSSDP with many linear constraints, however, is numerically challenging. This paper mainly shows the applicability of the classical alternating direction method (ADM) for solving LSSDP and convinces the efficiency of the ADM approach. We compare the ADM approach with some other existing approaches numerically, and we show the superiority of ADM for solving large-scale LSSDP.