We introduce a new Krylov subspace iteration for large scale eigen-value problems that is able to accelerate the convergence through an inexact (iterative) solution to a shift-invert equation. The method also takes full advantage of an exact solution when it is possible to apply a sparse direct method to solve the shift-invert equations. We call this new iteration the Truncated RQ iteration (TR...

This paper presents an example of an algebraic eigenvalue problem which can be used to motivate the study of numerical techniques for solving such problems. The problem consists of finding the axis and angle of rotation from a 3 x 3 rotation matrix and is referred to as the axis-angle problem. The problem is used to demonstrate the inverse power method for finding eigenvectors. The axis-angle p...

Technische Universit at Dresden Herausgeber: Der Rektor A Generalized Inverse Iteration for Computing Simple Eigenvalues of Nonsymmetric Matrices Hubert Schwetlick and Ralf L osche IOKOMO-07-97 December 1997 Preprint-Reihe IOKOMO der DFG-Forschergruppe Identi kation und Optimierung komplexer Modelle auf der Basis analytischer Sensitivitatsberechnungen an der Technischen Universitat Dresden ...

Power Method We now describe the power method for computing the dominant eigenpair. Its extension to the inverse power method is practical for finding any eigenvalue provided that a good initial approximation is known. Some schemes for finding eigenvalues use other methods that converge fast, but have limited precision. The inverse power method is then invoked to refine the numerical values and...

K.V. Fernando developed an efficient approach for computation of an eigenvector of a tridiagonal matrix corresponding to an approximate eigenvalue. We supplement Fernando’s method with deflation procedures by Givens rotations. These deflations can be used in the Lanczos process and instead of the inverse iteration.

In many infeasible linear programs it is important to construct it to a feasible problem with a minimum pa-rameters changing corresponding to a given nonnegative vector. This paper defines a new inverse problem, called “inverse feasible problem”. For a given infeasible polyhedron and an n-vector a minimum perturba-tion on the parameters can be applied and then a feasible polyhedron is concluded.

The aim of this paper is to provide a convergence analysis for a preconditioned subspace iteration, which is designated to determine a modest number of the smallest eigenvalues and its corresponding invariant subspace of eigenvectors of a large, symmetric positive definite matrix. The algorithm is built upon a subspace implementation of preconditioned inverse iteration, i.e., the well-known inv...

For the singular saddle-point problems with nonsymmetric positive definite (1, 1) block, we present a general constraint preconditioning (GCP) iteration method based on a singular constraint preconditioner. Using the properties of the Moore-Penrose inverse, the convergence properties of the GCP iteration method are studied. In particular, for each of the two different choices of the (1, 1) bloc...

We study methods for the numerical solution of the Helmholtz equation for twodimensional applications in geophysics. The common framework of the iterative methods in our study is a combination of an inner iteration with a geometric multigrid method used as a preconditioner and an outer iteration with a Krylov subspace method. The preconditioning system is based on either a pure or shifted Helmh...

this paper presents a hybrid technique for simultaneous estimation of parameter and function in inverse heat conduction problems. no prior information is used for the functional form of the heat flux in the present study. the scheme presented here is a combination of two different classical methods: the variable metric method (vmm) and gauss method (gm). the determination of the unknown thermal...