Regularization Methods for Semidefinite Programming
SIAM Journal on Optimization
A Newton-CG Augmented Lagrangian Method for Semidefinite Programming
SIAM Journal on Optimization
A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging
Journal of Mathematical Imaging and Vision
On the Complexity of the Hybrid Proximal Extragradient Method for the Iterates and the Ergodic Mean
SIAM Journal on Optimization
Copositive and semidefinite relaxations of the quadratic assignment problem
Discrete Optimization
Hi-index | 0.00 |
In this paper, we consider block-decomposition first-order methods for solving large-scale conic semidefinite programming problems given in standard form. Several ingredients are introduced to speed-up the method in its pure form such as: an aggressive choice of stepsize for performing the extragradient step; use of scaled inner products; dynamic update of the scaled inner product for properly balancing the primal and dual relative residuals; and proper choices of the initial primal and dual iterates, as well as the initial parameter for the scaled inner product. Finally, we present computational results showing that our method outperforms the two most competitive codes for large-scale conic semidefinite programs, namely: the boundary-point method introduced by Povh et al. and the Newton-CG augmented Lagrangian method by Zhao et al.