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

Chengjing Wang () and Peipei Tang ()
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Chengjing Wang: Southwest Jiaotong University
Peipei Tang: Zhejiang University City College

Computational Optimization and Applications, 2017, vol. 68, issue 3, No 3, 503-532

Abstract: Abstract 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.

Keywords: Majorized semismooth Newton-CG augmented Lagrangian method; Iteration complexity; Quadratic semidefinite programming (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s10589-017-9930-9

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