Arrowhead matrix

In the mathematical field of linear algebra, an arrowhead matrix is a square matrix containing zeros in all entries except for the first row, first column, and main diagonal, these entries can be any number.[1][2] In other words, the matrix has the form

Any symmetric permutation of the arrowhead matrix, , where P is a permutation matrix, is a (permuted) arrowhead matrix. Real symmetric arrowhead matrices are used in some algorithms for finding of eigenvalues and eigenvectors.[3]

Real symmetric arrowhead matrices

Let A be a real symmetric (permuted) arrowhead matrix of the form

where is diagonal matrix of order n-1,

is a vector and is a scalar. Let

be the eigenvalue decomposition of A, where

is a diagonal matrix whose diagonal elements are the eigenvalues of A, and

is an orthonormal matrix whose columns are the corresponding eigenvectors. The following holds:

  • If for some i, then the pair , where is the i-th standard basis vector, is an eigenpair of A. Thus, all such rows and columns can be deleted, leaving the matrix with all .
  • The Cauchy interlacing theorem implies that the sorted eigenvalues of A interlace the sorted elements : if (this can be attained by symmetric permutation of rows and columns without loss of generality), and if s are sorted accordingly, then .
  • If , for some , the above inequality implies that is an eigenvalue of A. The size of the problem can be reduced by annihilating with a Givens rotation in the -plane and proceeding as above.

Symmetric arrowhead matrices arise in descriptions of radiationless transitions in isolated molecules and oscillators vibrationally coupled with a Fermi liquid.[4]

Eigenvalues and eigenvectors

A symmetric arrowhead matrix is irreducible if for all i and for all . The eigenvalues of an irreducible real symmetric arrowhead matrix are the zeros of the secular equation

which can be, for example, computed by the bisection method. The corresponding eigenvectors are equal to

Direct application of the above formula may yield eigenvectors which are not numerically sufficiently orthogonal.[1] The forward stable algorithm which computes each eigenvalue and each component of the corresponding eigenvector to almost full accuracy is described in.[2] The Julia version of the software is available.[5]

Inverses

Let A be an irreducible real symmetric arrowhead matrix. If for some i, the inverse is a permuted irreducible real symmetric arrowhead matrix:

where


If for all i, the inverse is a rank-one modification of a diagonal matrix (diagonal-plus-rank-one matrix or DPR1):

where

References

  1. O'Leary, D. P.; Stewart, G. W. (1990). "Computing the eigenvalues and eigenvectors of symmetric arrowhead matrices". Journal of Computational Physics. 90 (2): 497–505. Bibcode:1990JCoPh..90..497O. doi:10.1016/0021-9991(90)90177-3.
  2. Jakovcevic Stor, Nevena; Slapnicar, Ivan; Barlow, Jesse L. (2015). "Accurate eigenvalue decomposition of real symmetric arrowhead matrices and applications". Linear Algebra and Its Applications. 464: 62–89. arXiv:1302.7203. doi:10.1016/j.laa.2013.10.007.
  3. Gu, Ming; Eisenstat, Stanley C. (1995). "A Divide-and-Conquer Algorithm for the Symmetric Tridiagonal Eigenproblem". SIAM Journal on Matrix Analysis and Applications. 16: 172–191. doi:10.1137/S0895479892241287.
  4. O'Leary, D.P.; Stewart, G.W. (October 1990). "Computing the eigenvalues and eigenvectors of symmetric arrowhead matrices" (PDF). Journal of Computational Physics. 90 (2): 497–505. Bibcode:1990JCoPh..90..497O. doi:10.1016/0021-9991(90)90177-3.
  5. "Arrowhead.jl"
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