In this article we apply reduced order techniques for the approximation of parametric eigenvalue problems. The effect of the choice of sampling points is investigated. Here we use the standard proper orthogonal decomposition technique to obtain the basis of the reduced space and Galerking orthogonal technique is used to get the reduced problem. We present some numerical results and observe that the use of sparse sampling is a good idea for sampling if the dimension of parameter space is high.

翻译：本文采用降阶技术来逼近参数化特征值问题，并研究了采样点选择的影响。我们使用标准本征正交分解技术获取降阶空间基，并采用Galerkin正交技术得到降阶问题。数值结果表明，当参数空间维度较高时，采用稀疏采样是一种有效的采样策略。