A primal majorized semismooth Newton-CG augmented Lagrangian method for large-scale linearly constrained convex programming

作者：Wang, Chengjing; Tang, Peipei^*

来源：Computational Optimization and Applications, 2017, 68(3): 503-532.

DOI：10.1007/s10589-017-9930-9

• Majorized semismooth Newton-CG augmented Lagrangian method
• Iteration complexity
• Quadratic semidefinite programming

In this paper, we propose a primal majorized semismooth Newton-CG augmented Lagrangian method for large-scale linearly constrained convex programming problems, especially for some difficult problems. The basic idea of this method is to apply the majorized semismooth Newton-CG augmented Lagrangian method to the primal convex problem. And we take two special nonlinear semidefinite programming problems as examples to illustrate the algorithm. Furthermore, we establish the global convergence and the iteration complexity of the algorithm. Numerical experiments demonstrate that our method works very well for the testing problems, especially for many ill-conditioned ones.

• 出版日期2017-12
• 单位浙江大学城市学院; 西南交通大学