Hessenberg matrix


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]

A Hessenberg decomposition is a matrix decomposition of a matrix into a unitary matrix and a Hessenberg matrix such that where denotes the conjugate transpose.



Upper Hessenberg matrix


A square   matrix   is said to be in upper Hessenberg form or to be an upper Hessenberg matrix if   for all   with  .

An upper Hessenberg matrix is called unreduced if all subdiagonal entries are nonzero, i.e. if   for all  .[3]

Lower Hessenberg matrix


A square   matrix   is said to be in lower Hessenberg form or to be a lower Hessenberg matrix if its transpose is an upper Hessenberg matrix or equivalently if   for all   with  .

A lower Hessenberg matrix is called unreduced if all superdiagonal entries are nonzero, i.e. if   for all  .



Consider the following matrices.      

The matrix   is an upper unreduced Hessenberg matrix,   is a lower unreduced Hessenberg matrix and   is a lower Hessenberg matrix but is not unreduced.

Computer programming


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 transformation 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.

Reduction to Hessenberg matrix


Householder transformations


Any   matrix can be transformed into a Hessenberg matrix by a similarity transformation using Householder transformations. The following procedure for such a transformation is adapted from A Second Course In Linear Algebra by Garcia & Roger.[4]

Let   be any real or complex   matrix, then let   be the   submatrix of   constructed by removing the first row in   and let   be the first column of  . Construct the   householder matrix   where  

This householder matrix will map   to   and as such, the block matrix   will map the matrix   to the matrix   which has only zeros below the second entry of the first column. Now construct   householder matrix   in a similar manner as   such that   maps the first column of   to  , where   is the submatrix of   constructed by removing the first row and the first column of  , then let   which maps   to the matrix   which has only zeros below the first and second entry of the subdiagonal. Now construct   and then   in a similar manner, but for the matrix   constructed by removing the first row and first column of   and proceed as in the previous steps. Continue like this for a total of   steps.

By construction of  , the first   columns of any   matrix are invariant under multiplication by   from the right. Hence, any matrix can be transformed to an upper Hessenberg matrix by a similarity transformation of the form  .

Jacobi (Givens) rotations


A Jacobi rotation (also called Givens rotation) is an orthogonal matrix transformation in the form


where  ,  , is the Jacobi rotation matrix with all matrix elements equal zero except for


One can zero the matrix element   by choosing the rotation angle   to satisfy the equation


Now, the sequence of such Jacobi rotations with the following  


reduces the matrix   to the lower Hessenberg form.[5]



For  , it is vacuously true that every   matrix is both upper Hessenberg, and lower Hessenberg.[6]

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

A matrix that is both upper Hessenberg and lower Hessenberg is a tridiagonal matrix, of which the Jacobi matrix is an important example. This includes the symmetric or Hermitian Hessenberg matrices. A Hermitian matrix can be reduced to tri-diagonal real symmetric matrices.[7]

Hessenberg operator


The Hessenberg operator is an infinite dimensional Hessenberg matrix. It commonly occurs as the generalization of the Jacobi operator to a system of orthogonal polynomials for the space of square-integrable holomorphic functions over some domain—that is, a Bergman space. In this case, the Hessenberg operator is the right-shift operator  , given by  

The eigenvalues of each principal submatrix of the Hessenberg operator are given by the characteristic polynomial for that submatrix. These polynomials are called the Bergman polynomials, and provide an orthogonal polynomial basis for Bergman space.

See also



  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
  3. ^ Horn & Johnson 1985, p. 35
  4. ^ Ramon Garcia, Stephan; Horn, Roger (2017). A Second Course In Linear Algebra. Cambridge University Press. ISBN 9781107103818.
  5. ^ Bini, Dario A.; Robol, Leonardo (2016). "Quasiseparable Hessenberg reduction of real diagonal plus low rank matrices and applications". Linear Algebra and Its Applications. 502: 186–213. arXiv:1501.07812. doi:10.1016/j.laa.2015.08.026.
  6. ^ Lecture Notes. Notes for 2016-10-21 Cornell University
  7. ^ "Computational Routines (eigenvalues) in LAPACK". sites.science.oregonstate.edu. Retrieved 2020-05-24.


  • Horn, Roger A.; Johnson, Charles R. (1985), Matrix Analysis, Cambridge University Press, ISBN 978-0-521-38632-6.
  • Stoer, Josef; Bulirsch, Roland (2002), Introduction to Numerical Analysis (3rd ed.), Berlin, New York: Springer-Verlag, ISBN 978-0-387-95452-3.
  • Press, WH; Teukolsky, SA; Vetterling, WT; Flannery, BP (2007), "Section 11.6.2. Reduction to Hessenberg Form", Numerical Recipes: The Art of Scientific Computing (3rd ed.), New York: Cambridge University Press, ISBN 978-0-521-88068-8
  • Hessenberg matrix at MathWorld.
  • Hessenberg matrix at PlanetMath.
  • High performance algorithms for reduction to condensed (Hessenberg, tridiagonal, bidiagonal) form
  • Algorithm overview