Feasible Semismooth Newton Method for a Class of Stochastic Linear Complementarity Problems

G. L. Zhou () and L. Caccetta ()
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G. L. Zhou: Curtin University of Technology
L. Caccetta: Curtin University of Technology

Journal of Optimization Theory and Applications, 2008, vol. 139, issue 2, No 11, 379-392

Abstract: Abstract We consider a class of stochastic linear complementarity problems (SLCPs) with finitely many realizations. In this paper we reformulate this class of SLCPs as a constrained minimization (CM) problem. Then, we present a feasible semismooth Newton method to solve this CM problem. Preliminary numerical results show that this CM reformulation may yield a solution with high safety for SLCPs.

Keywords: Stochastic linear complementarity problems; Constrained minimization; Feasible semismooth Newton methods (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10957-008-9406-2

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