A primal majorized semismooth Newton-CG augmented Lagrangian method for large-scale linearly constrained convex programming
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DOI: 10.1007/s10589-017-9930-9
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- Chengjing Wang, 2016. "On how to solve large-scale log-determinant optimization problems," Computational Optimization and Applications, Springer, vol. 64(2), pages 489-511, June.
- R. T. Rockafellar, 1976. "Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming," Mathematics of Operations Research, INFORMS, vol. 1(2), pages 97-116, May.
- DEVOLDER, Olivier & GLINEUR, François & NESTEROV, Yurii, 2011.
"First-order methods of smooth convex optimization with inexact oracle,"
LIDAM Discussion Papers CORE
2011002, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- DEVOLDER, Olivier & GLINEUR, François & NESTEROV, Yurii, 2014. "First-order methods of smooth convex optimization with inexact oracle," LIDAM Reprints CORE 2594, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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Keywords
Majorized semismooth Newton-CG augmented Lagrangian method; Iteration complexity; Quadratic semidefinite programming;All these keywords.
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