基于聚集的自适应AMG求解时间的改进
Improving Solve Time of aggregation-based adaptive AMG
摘要：
本文提出了提高求解时间的引导AMG的作者以前设计的。这是通过使用足够大的聚集体将由引导算法生成的代数平滑向量的信息集合合并在单个层次结构中来实现的，并且这些聚集体是已经在整个引导算法中构建的聚集体的组合物。改进的AMG方法具有良好的收敛性能，并显示出显着减少，内存和求解时间。这些节省相对于原来的引导AMG的一些困难（标准AMG）的线性系所产生的标量和矢量函数椭圆偏微分方程（PDE）的离散化在2D和3D。
英文摘要：
This paper proposes improving the solve time of a bootstrap AMG designed previously by the authors. This is achieved by incorporating the information, set of algebraically smooth vectors, generated by the bootstrap algorithm, in a single hierarchy by using sufficiently large aggregates, and these aggregates are compositions of aggregates already built throughout the bootstrap algorithm. The modified AMG method has good convergence properties and shows significant reduction in both, memory and solve time. These savings with respect to the original bootstrap AMG are illustrated on some difficult (for standard AMG) linear systems arising from discretization of scalar and vector function elliptic partial differential equations (PDEs) in both 2d and 3d.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
