首页> 美国政府科技报告 >New Algorithm for the Computation of the Smallest Eigenvalue of a SymmetricMatrix and Its Eigenspace
【24h】

New Algorithm for the Computation of the Smallest Eigenvalue of a SymmetricMatrix and Its Eigenspace

机译:计算对称矩阵及其特征空间最小特征值的新算法

获取原文

摘要

The problem of finding the smallest eigenvalue and the corresponding eigenspaceof a symmetric matrix is stated as a semidefinite optimization problem. A straightforward application of nowadays more or less standard routines for the solution of semidefinite problems yields a new algorithm for the smallest eigenvalue problem; the approach not only yields the smallest eigenvalue, but also a symmetric positive semidefinite (SPSD) matrix whose column space is equal to the eigenspace for the smallest eigenvalue. It is shown that the predictor-corrector method yields a polynomial time algorithm which, with a suitable choice of the step size, asymptotically is quadratically convergent.

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号