Symmetric Eigenvalue Problems: LAPACK Computational Routines

Developer Reference for Intel® oneAPI Math Kernel Library for Fortran

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Date 3/31/2025

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Symmetric Eigenvalue Problems: LAPACK Computational Routines

Symmetric eigenvalue problems are posed as follows: given an n-by-n real symmetric or complex Hermitian matrix A, find the eigenvalues λ and the corresponding eigenvectors z that satisfy the equation

Az = λz (or, equivalently, z^HA = λz^H).

In such eigenvalue problems, all n eigenvalues are real not only for real symmetric but also for complex Hermitian matrices A, and there exists an orthonormal system of n eigenvectors. If A is a symmetric or Hermitian positive-definite matrix, all eigenvalues are positive.

To solve a symmetric eigenvalue problem with LAPACK, you usually need to reduce the matrix to tridiagonal form and then solve the eigenvalue problem with the tridiagonal matrix obtained. LAPACK includes routines for reducing the matrix to a tridiagonal form by an orthogonal (or unitary) similarity transformation A = QTQ^H as well as for solving tridiagonal symmetric eigenvalue problems. These routines (for FORTRAN 77 interface) are listed in Table "Computational Routines for Solving Symmetric Eigenvalue Problems". The corresponding routine names in the Fortran 95 interface are without the first symbol.

There are different routines for symmetric eigenvalue problems, depending on whether you need all eigenvectors or only some of them or eigenvalues only, whether the matrix A is positive-definite or not, and so on.

These routines are based on three primary algorithms for computing eigenvalues and eigenvectors of symmetric problems: the divide and conquer algorithm, the QR algorithm, and bisection followed by inverse iteration. The divide and conquer algorithm is generally more efficient and is recommended for computing all eigenvalues and eigenvectors. Furthermore, to solve an eigenvalue problem using the divide and conquer algorithm, you need to call only one routine. In general, more than one routine has to be called if the QR algorithm or bisection followed by inverse iteration is used.

Computational Routines for Solving Symmetric Eigenvalue Problems
Operation	Real symmetric matrices	Complex Hermitian matrices
Reduce to tridiagonal form `A = QTQ^H` (full storage)	sytrd syrdb	hetrd herdb
Reduce to tridiagonal form `A = QTQ^H` (packed storage)	sptrd	hptrd
Reduce to tridiagonal form `A = QTQ^H` (band storage).	sbtrd	hbtrd
Generate matrix `Q` (full storage)	orgtr	ungtr
Generate matrix `Q` (packed storage)	opgtr	upgtr
Apply matrix `Q` (full storage)	ormtr	unmtr
Multiplies a general matrix by an orthogonal/unitary matrix with a 2x2 structure.	orm22	unm22
Apply matrix `Q` (packed storage)	opmtr	upmtr
Find all eigenvalues of a tridiagonal matrix `T`	sterf
Find all eigenvalues and eigenvectors of a tridiagonal matrix `T`	steqr stedc	steqr stedc
Find all eigenvalues and eigenvectors of a tridiagonal positive-definite matrix `T`.	pteqr	pteqr
Find selected eigenvalues of a tridiagonal matrix `T`	stebz stegr	stegr
Find selected eigenvectors of a tridiagonal matrix `T`	stein stegr	stein stegr
Find selected eigenvalues and eigenvectors of f a real symmetric tridiagonal matrix `T`	stemr	stemr
Compute the reciprocal condition numbers for the eigenvectors	disna	disna

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Developer Reference for Intel® oneAPI Math Kernel Library for Fortran

Symmetric Eigenvalue Problems: LAPACK Computational Routines