Abstract
Our purpose of this paper is to solve a class of stochastic linear complementarity problems (SLCP) with finitely many elements. Based on a new stochastic linear complementarity problem function, a new semi-smooth least squares reformulation of the stochastic linear complementarity problem is introduced. For solving the semi-smooth least squares reformulation, we propose a feasible nonsmooth Levenberg–Marquardt-type method. The global convergence properties of the nonsmooth Levenberg–Marquardt-type method are also presented. Finally, the related numerical results illustrate that the proposed method is efficient for the related refinery production problem and the large-scale stochastic linear complementarity problems.
Highlights
Suppose (Ω, F, P) is a probability space with Ω ⊆
We make a numerical comparison between Method 1 and the scaled trust region method (STRM) in [20]
We implement Method 1 in MATLAB and test the method on the given test problems using the reformulation from the previous section
Summary
Suppose (Ω, F, P) is a probability space with Ω ⊆
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