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A stopping criterion for the conjugate gradient algorithm in the framework of anisotropic adaptive finite elements

机译:各向异性自适应有限元框架下共轭梯度算法的停止准则

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We propose a simple stopping criterion for the conjugate gradient (CG) algorithm in the framework of anisotropic, adaptive finite elements for elliptic problems. The goal of the adaptive algorithm is to find a triangulation such that the estimated relative error is close to a given tolerance TOL. We propose to stop the CG algorithm whenever the residual vector has Euclidian norm less than a small fraction of the estimated error. This stopping criterion is based on a posteriori error estimates between the true solution u and the computed solution u_h~n (the superscript n stands for the CG iteration number, the subscript h for the typical mesh size) and on heuristics to relate the error between u_h and u_h~n to the residual vector.rnNumerical experiments with anisotropic adaptive meshes show that the total number of CG iterations can be divided by 10 without significant discrepancy in the computed results.
机译:我们为各向异性问题的各向异性自适应有限元框架提出了共轭梯度(CG)算法的简单停止准则。自适应算法的目标是找到一个三角剖分,使估计的相对误差接近给定的公差TOL。我们建议只要残差矢量的欧几里得范数小于估计误差的一小部分,就停止CG算法。该停止标准基于真实解u与计算出的解u_h〜n之间的后验误差估计(上标n代表CG迭代次数,下标h代表典型的网格大小),并基于启发式方法将误差u_h和u_h〜n到残差向量。各向异性自适应网格的数值实验表明,CG迭代的总数可以除以10,而计算结果却没有显着差异。

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