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A Lipschitzian Error Bound for Convex Quadratic Symmetric Cone Programming

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Abstract

Under a Slater-type condition, we reformulate a convex quadratic symmetric cone programming problem as an unconstrained eigenvalue minimization problem, and obtain a sufficient condition ensuring the existence of a Lipschitzian error bound for the eigenvalue minimization problem. This error bound, in turn, provides an estimation of the distance from a feasible solution of the convex quadratic symmetric cone program to its optimal solution set.

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Acknowledgements

This paper is a revised version of a part of the author’s 2012 PhD dissertation at Nanyang Technological University, Singapore.

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Authors and Affiliations

  1. College of Mathematics and Computer Science, Fujian Normal University, Fuzhou, 350007, P.R. China

    Huiling Lin

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  1. Huiling Lin
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Correspondence to Huiling Lin.

Additional information

Supported by the Scientific Research Foundation For Returned Scholars, Ministry of Education of China, Foundation of the Education Department of Fujian Province (No. JA15106), the project for nonlinear analysis and its applications (No. IRTL1206), and National Natural Science Foundations of China (No. 11301080).

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Lin, H. A Lipschitzian Error Bound for Convex Quadratic Symmetric Cone Programming. Acta Appl Math 144, 17–34 (2016). https://doi.org/10.1007/s10440-015-0037-y

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  • DOI: https://doi.org/10.1007/s10440-015-0037-y

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