摘要 : In this paper we study inexact inverse iteration for solving the generalised eigenvalue problem Ax = λMx. We show that inexact inverse iteration is a modified Newton method and hence obtain convergence rates for various versions o... 展开 In this paper we study inexact inverse iteration for solving the generalised eigenvalue problem Ax = λMx. We show that inexact inverse iteration is a modified Newton method and hence obtain convergence rates for various versions of inexact inverse iteration for the calculation of an algebraically simple eigenvalue. In particular, if the inexact solves are carried out with a tolerance chosen proportional to the eigenvalue residual then quadratic convergence is achieved. We also show how modifying the right hand side in inverse iteration still provides a convergent method, but the rate of convergence will be quadratic only under certain conditions on the right hand side. We discuss the implications of this for the preconditioned iterative solution of the linear systems. Finally we introduce a new ILU preconditioner which is a simple modification to the usual preconditioner, but which has advantages both for the standard form of inverse iteration and for the version with a modified right hand side. Numerical examples are given to illustrate the theoretical results. 收起