首页> 外文期刊>Journal of industrial and management optimization >ASYMPTOTIC CONVERGENCE OF STATIONARY POINTS OF STOCHASTIC MULTIOBJECTIVE PROGRAMS WITH PARAMETRIC VARIATIONAL INEQUALITY CONSTRAINT VIA SAA APPROACH
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ASYMPTOTIC CONVERGENCE OF STATIONARY POINTS OF STOCHASTIC MULTIOBJECTIVE PROGRAMS WITH PARAMETRIC VARIATIONAL INEQUALITY CONSTRAINT VIA SAA APPROACH

机译:参数变分不等式约束的随机多目标程序平稳点的渐近收敛性(通过SAA方法)

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摘要

We consider the sample average approximation method for a stochastic multiobjective programming problem constrained by parametric variational inequalities. The first order necessary conditions for the original problem and its sample average approximation (SAA) problem are established under constraint qualifications. By graphical convergence of set-valued mappings, the stationary points of the SAA problem converge to the stationary points of the true problem when the sample size tends to infinity. Under proper assumptions, the convergence of optimal solutions of SAA problems is also obtained. The numerical experiments on stochastic multiobjective optimization problems with variational inequalities are given to illustrate the efficiency of SAA estimators.
机译:我们考虑了一个随机多目标规划问题的样本均值逼近方法,该问题受参数变分不等式的约束。在约束条件下建立了原始问题及其样本平均近似(SAA)问题的一阶必要条件。通过集值映射的图形收敛,当样本量趋于无穷大时,SAA问题的平稳点收敛到真实问题的平稳点。在适当的假设下,也可以获得SAA问题的最优解的收敛性。给出了具有变分不等式的随机多目标优化问题的数值实验,以说明SAA估计的效率。

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