Please use this identifier to cite or link to this item:
|Title:||A newton-cg augmented lagrangian method for semidefinite programming|
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on May 23, 2018
WEB OF SCIENCETM
checked on May 16, 2018
checked on May 18, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.