Spectral Gradient Algorithm Based on the Generalized Fiser-Burmeister Function for Sparse Solutions of LCPS

Chang Gao; Zhensheng Yu; Feiran Wang

doi:10.4236/ojs.2015.56057

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Open Journal of Statistics > Vol.5 No.6, October 2015

Spectral Gradient Algorithm Based on the Generalized Fiser-Burmeister Function for Sparse Solutions of LCPS ()

Chang Gao, Zhensheng Yu, Feiran Wang

College of Science, University of Shanghai for Science and Technology, Shanghai, China.

DOI: 10.4236/ojs.2015.56057 PDF HTML XML 1,796 Downloads 2,103 Views

Abstract

This paper considers the computation of sparse solutions of the linear complementarity problems LCP(q, M). Mathematically, the underlying model is NP-hard in general. Thus an lp(0 < p < 1) regularized minimization model is proposed for relaxation. We establish the equivalent unconstrained minimization reformation of the NCP-function. Based on the generalized Fiser-Burmeister function, a sequential smoothing spectral gradient method is proposed to solve the equivalent problem. Numerical results are given to show the efficiency of the proposed method.

Keywords

Linear Complementarity Problem, Sparse Solution, Spectral Gradient, Generalized Fischer-Burmeister

Share and Cite:

Gao, C. , Yu, Z. and Wang, F. (2015) Spectral Gradient Algorithm Based on the Generalized Fiser-Burmeister Function for Sparse Solutions of LCPS. Open Journal of Statistics, 5, 543-551. doi: 10.4236/ojs.2015.56057.

Conflicts of Interest

The authors declare no conflicts of interest.

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