Please use this identifier to cite or link to this item: https://doi.org/10.1137/080718206
Title: A newton-cg augmented lagrangian method for semidefinite programming
Authors: Zhao, X.-Y.
Sun, D. 
Toh, K.-C. 
Keywords: Augmented lagrangian
Iterative solver
Newton method
Semidefinite programming
Semismoothness
Issue Date: 2010
Citation: Zhao, X.-Y., Sun, D., Toh, K.-C. (2010). A newton-cg augmented lagrangian method for semidefinite programming. SIAM Journal on Optimization 20 (4) : 1737-1765. ScholarBank@NUS Repository. https://doi.org/10.1137/080718206
Abstract: We consider a Newton-CG augmented Lagrangian method for solving semidefinite programming (SDP) problems from the perspective of approximate semismooth Newton methods. In order to analyze the rate of convergence of our proposed method, we characterize the Lipschitz continuity of the corresponding solution mapping at the origin. For the inner problems, we show that the positive definiteness of the generalized Hessian of the objective function in these inner problems, a key property for ensuring the efficiency of using an inexact semismooth Newton-CG method to solve the inner problems, is equivalent to the constraint nondegeneracy of the corresponding dual problems. Numerical experiments on a variety of large-scale SDP problems with the matrix dimension n up to 4, 110 and the number of equality constraints m up to 2, 156, 544 show that the proposed method is very efficient. We are also able to solve the SDP problem fap36 (with n = 4, 110 and m = 1, 154, 467) in the Seventh DIMACS Implementation Challenge much more accurately than in previous attempts. Copyright © 2010, Society for Industrial and Applied Mathematics.
Source Title: SIAM Journal on Optimization
URI: http://scholarbank.nus.edu.sg/handle/10635/102695
ISSN: 10526234
DOI: 10.1137/080718206
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

125
checked on Oct 24, 2018

WEB OF SCIENCETM
Citations

112
checked on Oct 8, 2018

Page view(s)

201
checked on Oct 19, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.