Quadratic eigenvalue problem

From Wikipedia, the free encyclopedia
Jump to: navigation, search

In mathematics, the quadratic eigenvalue problem[1] (QEP), is to find scalar eigenvalues \lambda\,, left eigenvectors y\, and right eigenvectors x\, such that

 Q(\lambda)x = 0\text{ and }y^\ast Q(\lambda) = 0,\,

where Q(\lambda)=\lambda^2 A_2 + \lambda A_1 + A_0\,, with matrix coefficients A_2, \, A_1, A_0 \in \mathbb{C}^{n \times n} and we require that A_2\,\neq 0, (so that we have a nonzero leading coefficient). There are 2n\, eigenvalues that may be infinite or finite, and possibly zero. This is a special case of a nonlinear eigenproblem. Q(\lambda) is also known as a quadratic matrix polynomial.


A QEP can result in part of the dynamic analysis of structures discretized by the finite element method. In this case the quadratic, Q(\lambda)\, has the form Q(\lambda)=\lambda^2 M + \lambda C + K\,, where M\, is the mass matrix, C\, is the damping matrix and K\, is the stiffness matrix. Other applications include vibro-acoustics and fluid dynamics.

Methods of solution[edit]

Direct methods for solving the standard or generalized eigenvalue problems  Ax = \lambda  x and  Ax = \lambda B x are based on transforming the problem to Schur or Generalized Schur form. However, there is no analogous form for quadratic matrix polynomials. One approach is to transform the quadratic matrix polynomial to a linear matrix pencil ( A-\lambda B), and solve a generalized eigenvalue problem. Once eigenvalues and eigenvectors of the linear problem have been determined, eigenvectors and eigenvalues of the quadratic can be determined.

The most common linearization is the first companion linearization

L(\lambda) = 
M & 0 \\
0 & I_n 
C & K \\
-I_n & 0 

where I_n is the n-by-n identity matrix, with corresponding eigenvector

z = 
\lambda x \\

We solve  L(\lambda) z = 0 for  \lambda and z, for example by computing the Generalized Schur form. We can then take the first n components of z as the eigenvector x of the original quadratic Q(\lambda).


  1. ^ F. Tisseur and K. Meerbergen, The quadratic eigenvalue problem, SIAM Rev., 43 (2001), pp. 235–286.