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Sample Average Approximation Methods for Solving Stochastic Mixed Complementarity Problems

Author: HeZhiFeng
Tutor: LinGuiHua
School: Dalian University of Technology
Course: Operational Research and Cybernetics
Keywords: Randomly mixed complementarity problem Sampling average approximation method Monte Carlo method Expected Value Model Expected residual minimization model Traffic equilibrium problem
CLC: O221
Type: Master's thesis
Year: 2010
Downloads: 49
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Abstract


In this paper, two of mixed complementarity problems Stochastic sampling average approximation method : 1 . Expected value ( EV ) method : Firstly, randomly mixed complementarity problem EV model , further use of the so-called Fischer - Burmeister function EV model is transformed into a non - smooth equations groups. Then use the sample average approximation method based on the Monte Carlo method , combined with semi - smooth Newton method to solve the equations , and the convergence analysis . Finally, this approach is applied to the traffic equilibrium problem , and a simple numerical experiments . Expectations residual minimization ( ERM ) method : First, the Fischer - Burmeister function of punishment the tectonic random mixed complementarity problem ERM model . Then consider the level set for the ERM model sector and the relative error bound . Subsequently, based on the Monte Carlo method of sampling average approximation method gives the ERM model approximation , and gives the convergence analysis . Finally, three simple numerical example to verify the feasibility of the method .

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CLC: > Mathematical sciences and chemical > Mathematics > Operations Research > Planning Theory ( mathematical programming)
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