Hessenberg matrix

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

In linear algebra, a Hessenberg matrix is a special kind of square matrix, one that is "almost" triangular. To be exact, an upper Hessenberg matrix has zero entries below the first subdiagonal, and a lower Hessenberg matrix has zero entries above the first superdiagonal.[1] They are named after Karl Hessenberg.[2]

For example:

1 & 4 & 2 & 3 \\
3 & 4 & 1 & 7 \\
0 & 2 & 3 & 4 \\
0 & 0 & 1 & 3 \\

is upper Hessenberg and

1 & 2 & 0 & 0 \\
5 & 2 & 3 & 0 \\
3 & 4 & 3 & 7 \\
5 & 6 & 1 & 1 \\

is lower Hessenberg.

Computer programming[edit]

Many linear algebra algorithms require significantly less computational effort when applied to triangular matrices, and this improvement often carries over to Hessenberg matrices as well. If the constraints of a linear algebra problem do not allow a general matrix to be conveniently reduced to a triangular one, reduction to Hessenberg form is often the next best thing. In fact, reduction of any matrix to a Hessenberg form can be achieved in a finite number of steps (for example, through Householder's algorithm of unitary similarity transforms). Subsequent reduction of Hessenberg matrix to a triangular matrix can be achieved through iterative procedures, such as shifted QR-factorization. In eigenvalue algorithms, the Hessenberg matrix can be further reduced to a triangular matrix through Shifted QR-factorization combined with deflation steps. Reducing a general matrix to a Hessenberg matrix and then reducing further to a triangular matrix, instead of directly reducing a general matrix to a triangular matrix, often economizes the arithmetic involved in the QR algorithm for eigenvalue problems.


The product of a Hessenberg matrix with a triangular matrix is again Hessenberg. More precisely, if A is upper Hessenberg and T is upper triangular, then AT and TA are upper Hessenberg.

A matrix that is both upper Hessenberg and lower Hessenberg is a tridiagonal matrix.

See also[edit]


  1. ^ Horn & Johnson (1985), page 28; Stoer & Bulirsch (2002), page 251
  2. ^ Biswa Nath Datta (2010) Numerical Linear Algebra and Applications, 2nd Ed., Society for Industrial and Applied Mathematics (SIAM) ISBN 978-0-89871-685-6, p. 307


External links[edit]