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- Title
An Inexact SQP Newton Method for Convex SC<sup>1</sup> Minimization Problems.
- Authors
Chen, Y. D.; Gao, Y.; Liu, Y.-J.
- Abstract
In this paper, we present a globally and superlinearly convergent inexact SQP Newton method for solving large scale convex SC1 minimization problems under mild conditions. In particular, the BD-regularity assumption made by Pang and Qi in Journal of Optimization Theory and Applications, 85 (), 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.
- Subjects
NEWTON-Raphson method; ESTIMATION theory; MATHEMATICAL statistics; STATISTICAL correlation; CURVE fitting
- Publication
Journal of Optimization Theory & Applications, 2010, Vol 146, Issue 1, p33
- ISSN
0022-3239
- Publication type
Article
- DOI
10.1007/s10957-010-9654-9