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A class of smoothing SAA methods for a stochastic linear complementarity problem
Shaolin Ji , Xiaole Xue. A stochastic maximum principle for linear quadratic problem with nonconvex control domain. American Institute of Mathematical Sciences. Previous Article Identification of water quality model parameters using artificial bee colony algorithm.
An efficient algorithm for convex quadratic semi-definite optimization. A class of smoothing sample average approximation SAA methods is proposed for solving a stochastic linear complementarity problem, where the underlying function is the expected value of stochastic function. Existence and convergence results to the proposed methods are provided and some numerical results are reported to show the efficiency of the methods proposed.
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DML-CZ - Czech Digital Mathematics Library: Prediction in stochastic linear programming
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