关于最小二乘拟合的 Succesive over Relaxation渐进迭代逼近
The Succesive Over Relaxation Progressive Iterative Approximation for Least Squares Fitting
DOI: 10.12677/AAM.2023.1211474, PDF, 下载: 148  浏览: 181  科研立项经费支持
作者: 田沂*：长沙理工大学数学与统计学院,湖南长沙；杜勇奇：哈尔滨工程大学数学科学学院,黑龙江哈尔滨
关键词: 渐进迭代逼近； Guass-Seidel迭代法；Succesive Over Relaxation迭代法；曲线逼近；Progressive Iterative Approximation； Guass-Seidel Iterative Method； Succesive Over Relaxation Iterative Method； Curve Fitting
摘要: 本文以Guass-Seidel progressive iterative approximation for least squares fitting( LSPLA)算法为基础，提出一种基于 Succesive Over Relaxation(SOR)迭代的 LSPIA算法,简称SOR- LSPIA。我们分析了SOR- LSPIA算法的收敛性，数值实验表明，当拟合精度相同时，SOR- LSPIA算法比GS- LSPIA算法送代步数更少、运行时间更短。
Abstract: Based on the Guass-Seidel progressive iterative approximation for least squares fitting( LSPLA) algorithm , a Succesive Over Relaxation LSPIA ( SOR-LSPIA ) algorithm is proposed in this paper. We analysis the convergence of this. Furthermore some numerical is tests experiments are shown, our aIgorithm has fewer number iteration steps and shorter cpu time than the GS-LSPIA algorithm does if the fitting accracies are requared the same.
文章引用：田沂, 杜勇奇. 关于最小二乘拟合的 Succesive over Relaxation渐进迭代逼近[J]. 应用数学进展, 2023, 12(11): 4806-4813. https://doi.org/10.12677/AAM.2023.1211474

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