{"title":"代数特征值问题解的初等旋转压缩","authors":"H. Rutishauser","doi":"10.1145/612201.612240","DOIUrl":null,"url":null,"abstract":"For the case, that an eigenvector v of an arbitr~ real matrix A is known, several Authors have proposed methods to elimi~ nate the corresponding eigenvalue from the matrix, either by changing it to zero or bY reducing the order of the matrix by one° The present paper derives from the components v[k] of v a set of elementary (two-dimensional) orthogonal transformations~ which transform A into a matrix A I whose last column has only the diagonal element different from zero. The rules are as follows Start with B o = A, then transform for k = 1,2,..o~n~I Bk_ I into B k = U~ Bk_IUk , where U k is obtained from the unit matrix by replacing the following elements The element [k,k] by cos(t[k]) [k+1,k+1] by cos(t[k]) [k,k+1] by sin(t[k]) [k+1 ,k] by sin(t[k]) , and the rotation angle t[k] is determined by tg(t[k]) = ~ v[i] ~ /v[k+1 ] • Then Bn_ I will be the required matrix A I having zeros in the This process may be continued with the matrix A I until after n(n-1)/2 rotations, A is transformed to triangular form (but between a total of n-1 eigenvectors must be computed). This is certainly a nearly trivial modification of Jacobi's or Greenstadt's method. It has however some decisive advantages: a) If not all eigenvalues are wanted, the process may be stopped earlier. b) The convergence behavior of Greenstadt's method is still not quite known, whereas the method given here is iterative only with respect to the computation of the eigenvectors, but this is a well known procedure.","PeriodicalId":109454,"journal":{"name":"ACM '59","volume":"78 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1959-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Deflation by elementary rotations for the solution of algebraic eigenvalue problems\",\"authors\":\"H. Rutishauser\",\"doi\":\"10.1145/612201.612240\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For the case, that an eigenvector v of an arbitr~ real matrix A is known, several Authors have proposed methods to elimi~ nate the corresponding eigenvalue from the matrix, either by changing it to zero or bY reducing the order of the matrix by one° The present paper derives from the components v[k] of v a set of elementary (two-dimensional) orthogonal transformations~ which transform A into a matrix A I whose last column has only the diagonal element different from zero. The rules are as follows Start with B o = A, then transform for k = 1,2,..o~n~I Bk_ I into B k = U~ Bk_IUk , where U k is obtained from the unit matrix by replacing the following elements The element [k,k] by cos(t[k]) [k+1,k+1] by cos(t[k]) [k,k+1] by sin(t[k]) [k+1 ,k] by sin(t[k]) , and the rotation angle t[k] is determined by tg(t[k]) = ~ v[i] ~ /v[k+1 ] • Then Bn_ I will be the required matrix A I having zeros in the This process may be continued with the matrix A I until after n(n-1)/2 rotations, A is transformed to triangular form (but between a total of n-1 eigenvectors must be computed). This is certainly a nearly trivial modification of Jacobi's or Greenstadt's method. It has however some decisive advantages: a) If not all eigenvalues are wanted, the process may be stopped earlier. b) The convergence behavior of Greenstadt's method is still not quite known, whereas the method given here is iterative only with respect to the computation of the eigenvectors, but this is a well known procedure.\",\"PeriodicalId\":109454,\"journal\":{\"name\":\"ACM '59\",\"volume\":\"78 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1959-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM '59\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/612201.612240\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM '59","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/612201.612240","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
的情况下,一个特征向量v的arbitr ~真正的矩阵是已知的,一些作者提出的方法elimi ~内特相应的矩阵的特征值,通过改变它为零或降低矩阵的顺序由一个°本文来源于组件v (k)的一组基本(二维)正交变换转换成矩阵A的~我的最后一列只有对角元素不同于零。规则如下:从B 0 = A开始,然后变换k = 1,2,…o ~ n ~我Bk_到B k = U ~ Bk_IUk, U k在哪里获得的单位矩阵代替以下元素的元素(k, k)因为(t [k]) (k + 1, k + 1),因为(t [k]) [k, k + 1]的罪(t [k]) (k + 1, k)的罪(t [k])和旋转角度t [k]是由tg (t [k]) = ~ v[我]~ / [k + 1]•然后Bn_我将所需的矩阵在这个过程可能会持续零矩阵A我直到n (n - 1) / 2转动,A被转换为三角形形式(但必须计算总共n-1个特征向量)。这当然是对Jacobi或Greenstadt方法的一个几乎微不足道的修改。然而,它有一些决定性的优点:a)如果不是所有的特征值都需要,进程可能会提前停止。b) Greenstadt方法的收敛性尚不完全清楚,而这里给出的方法仅对特征向量的计算是迭代的,但这是一个众所周知的过程。
Deflation by elementary rotations for the solution of algebraic eigenvalue problems
For the case, that an eigenvector v of an arbitr~ real matrix A is known, several Authors have proposed methods to elimi~ nate the corresponding eigenvalue from the matrix, either by changing it to zero or bY reducing the order of the matrix by one° The present paper derives from the components v[k] of v a set of elementary (two-dimensional) orthogonal transformations~ which transform A into a matrix A I whose last column has only the diagonal element different from zero. The rules are as follows Start with B o = A, then transform for k = 1,2,..o~n~I Bk_ I into B k = U~ Bk_IUk , where U k is obtained from the unit matrix by replacing the following elements The element [k,k] by cos(t[k]) [k+1,k+1] by cos(t[k]) [k,k+1] by sin(t[k]) [k+1 ,k] by sin(t[k]) , and the rotation angle t[k] is determined by tg(t[k]) = ~ v[i] ~ /v[k+1 ] • Then Bn_ I will be the required matrix A I having zeros in the This process may be continued with the matrix A I until after n(n-1)/2 rotations, A is transformed to triangular form (but between a total of n-1 eigenvectors must be computed). This is certainly a nearly trivial modification of Jacobi's or Greenstadt's method. It has however some decisive advantages: a) If not all eigenvalues are wanted, the process may be stopped earlier. b) The convergence behavior of Greenstadt's method is still not quite known, whereas the method given here is iterative only with respect to the computation of the eigenvectors, but this is a well known procedure.