An Inexact SQP Newton Method for Convex SC1 Minimization Problems

  • Y. D. Chen
  • Y. Gao
  • Y.-J. Liu


In this paper, we present a globally and superlinearly convergent inexact SQP Newton method for solving large scale convex SC 1 minimization problems under mild conditions. In particular, the BD-regularity assumption made by Pang and Qi in Journal of Optimization Theory and Applications, 85 (1995), pp. 633–648 is replaced by a much more realistic assumption. Our numerical experiments conducted on least squares semidefinite programming with lower and upper bounds demonstrate that our inexact SQP Newton method is much more efficient than its exact version and is competitive with existing methods when the number of simple constraints is very large.


Convex SC1 minimization Inexact SQP method Semismoothness Superlinear convergence 


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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Royal Bank of Scotland, 38/F, Cheung Kong CenterHong KongHong Kong
  2. 2.Department of MathematicsNational University of SingaporeSingaporeRepublic of Singapore
  3. 3.School of ScienceShenyang Institute of Aeronautical EngineeringShenyangP.R. China

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