An Inexact SQP Newton Method for Convex SC1 Minimization Problems

Y. D. Chen (), Y. Gao () and Y.-J. Liu ()
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Y. D. Chen: Royal Bank of Scotland, 38/F, Cheung Kong Center
Y. Gao: National University of Singapore
Y.-J. Liu: Shenyang Institute of Aeronautical Engineering

Journal of Optimization Theory and Applications, 2010, vol. 146, issue 1, No 3, 33-49

Abstract: Abstract 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.

Keywords: Convex SC 1 minimization; Inexact SQP method; Semismoothness; Superlinear convergence (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/s10957-010-9654-9

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