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template<typename T>
void syevr(char *jobz, char *range, char *uplo, integer *n, T *a, integer *lda, T *vl, T *vu, integer *il, integer *iu, T *abstol, integer *m, T *w, T *z, integer *ldz, integer *isuppz, T *work, integer *lwork, integer *iwork, integer *liwork, integer *info)# Hermitian eigenvalue decomposition (MRRR)
Purpose:
Hermitian eigenvalue decomposition (MRRR). Computation of eigenvalues and, optionally, the left and/or right eigenvectors for SY matrices SSYEVR computes selected eigenvalues and, optionally, eigenvectors of a real symmetric matrix a. Eigenvalues and eigenvectors can be selected by specifying either a range of values or a range of indices for the desired eigenvalues. SSYEVR first reduces the matrix a to tridiagonal form T with a call to SSYTRD. Then, whenever possible, SSYEVR calls SSTEMR to compute the eigenspectrum using Relatively Robust Representations. SSTEMR computes eigenvalues by the dqds algorithm, while orthogonal eigenvectors are computed from various "good" L D L^T representations (also known as Relatively Robust Representations). Gram-Schmidt orthogonalization is avoided as far as possible. More specifically, the various steps of the algorithm are as follows. For each unreduced block (submatrix) of T, (a) Compute T - sigma I = L D L^T, so that L and D define all the wanted eigenvalues to high relative accuracy. This means that small relative changes in the entries of D and L cause only small relative changes in the eigenvalues and eigenvectors. The standard (unfactored) representation of the tridiagonal matrix T does not have this property in general. (b) Compute the eigenvalues to suitable accuracy. If the eigenvectors are desired, the algorithm attains full accuracy of the computed eigenvalues only right before the corresponding vectors have to be computed, see steps c) and d). (c) For each cluster of close eigenvalues, select a new shift close to the cluster, find a new factorization, and refine the shifted eigenvalues to suitable accuracy. (d) For each eigenvalue with a large enough relative separation compute the corresponding eigenvector by forming a rank revealing twisted factorization. Go back to (c) for any clusters that remain. The desired accuracy of the output can be specified by the input parameter abstol. For more details, see SSTEMR's documentation and: - Inderjit S. Dhillon and Beresford N. Parlett: "Multiple representations to compute orthogonal eigenvectors of symmetric tridiagonal matrices," Linear Algebra and its Applications, 387(1), pp. 1-28, August 2004. - Inderjit Dhillon and Beresford Parlett: "Orthogonal Eigenvectors and Relative Gaps," SIAM Journal on Matrix Analysis and Applications, Vol. 25, 2004. Also LAPACK Working Note 154. - Inderjit Dhillon: "A new O(n^2) algorithm for the symmetric tridiagonal eigenvalue/eigenvector problem", Computer Science Division Technical Report No. UCB/CSD-97-971, UC Berkeley, May 1997. Note 1 : SSYEVR calls SSTEMR when the full spectrum is requested on machines which conform to the ieee-754 floating point standard. SSYEVR calls SSTEBZ and SSTEIN on non-ieee machines and when partial spectrum requests are made. Normal execution of SSTEMR may create NaNs and infinities and hence may abort due to a floating point exception in environments which do not handle NaNs and infinities in the ieee standard default manner.
- Parameters:
jobz – [in]
jobz is char*
= ‘N’: Compute eigenvalues only;
= ‘V’: Compute eigenvalues and eigenvectors.range – [in]
range is char*
= ‘A’: all eigenvalues will be found.
= ‘V’: all eigenvalues in the half-open interval (vl,vu] will be found.
= ‘I’: the il-th through iu-th eigenvalues will be found.
For range = ‘V’ or ‘I’ and iu - il < N - 1, SSTEBZ and SSTEIN are calleduplo – [in]
uplo is char*
= ‘U’: Upper triangle of a is stored;
= ‘L’: Lower triangle of a is stored.n – [in]
n is integer*
The order of the matrix a. n >= 0.
a – [inout]
a is float/double array, dimension (lda, n)
On entry, the symmetric matrix a. If uplo = ‘U’, the leading n-by-n upper triangular part of a contains the upper triangular part of the matrix a. If uplo = ‘L’, the leading n-by-n lower triangular part of a contains the lower triangular part of the matrix a.
On exit, the lower triangle (if uplo=’L’) or the upper triangle (if uplo=’U’) of A, including the diagonal, is destroyed.lda – [in]
lda is integer*
The leading dimension of the array a. lda >= fla_max(1,n).
vl – [in]
vl is float/double*
If range=’V’, the lower bound of the interval to be searched for eigenvalues. vl < vu.
Not referenced if range = ‘A’ or ‘I’.vu – [in]
vu is float/double*
If range=’V’, the upper bound of the interval to be searched for eigenvalues. vl < vu.
Not referenced if range = ‘A’ or ‘I’.il – [in]
il is integer*
If range=’I’, the index of the smallest eigenvalue to be returned.
1 <= il <= iu <= N, if N > 0; il = 1 and iu = 0 if N = 0.
Not referenced if range = ‘A’ or ‘V’.
iu – [in]
iu is integer*
If range=’I’, the index of the largest eigenvalue to be returned.
1 <= il <= iu <= N, if N > 0; il = 1 and iu = 0 if N = 0.
Not referenced if range = ‘A’ or ‘V’.
abstol – [in]
abstol is float/double*
The absolute error tolerance for the eigenvalues.
An approximate eigenvalue is accepted as converged when it is determined to lie in an interval [a,b] of width less than or equal to
abstol + EPS * fla_max( |a|,|b|) ,
where EPS is the machine precision. If abstol is less than or equal to zero, then EPS*|T| will be used in its place, where |T| is the 1-norm of the tridiagonal matrix obtained by reducing A to tridiagonal form.See “Computing Small Singular Values of Bidiagonal Matrices
with Guaranteed High Relative Accuracy,” by Demmel and Kahan, LAPACK Working Note #3.
If high relative accuracy is important, set abstol to SLAMCH( ‘Safe minimum’). Doing so will guarantee that eigenvalues are computed to high relative accuracy when possible in future releases. The current code does not make any guarantees about high relative accuracy, but future releases will. See J. Barlow and J. Demmel, “Computing Accurate Eigensystems of Scaled Diagonally
Dominant Matrices”, LAPACK Working Note #7, for a discussion of which matrices define their eigenvalues to high relative accuracy.
m – [out]
m is integer*
The total number of eigenvalues found. 0 <= m <= n.
If range = ‘A’, m = n, and if range = ‘I’, m = iu-il+1.w – [out]
w is float/double/COMPLEX/COMPLEX*16 array, dimension (n)
The first m elements contain the selected eigenvalues in ascending order.
z – [out]
z is float/double array, dimension (ldz, fla_max(1,m))
If jobz = ‘V’, then if info = 0, the first M columns of Z contain the orthonormal eigenvectors of the matrix a corresponding to the selected eigenvalues, with the i-th column of Z holding the eigenvector associated with W(i).
If jobz = ‘N’, then Z is not referenced.
Note: the user must ensure that at least fla_max(1,m) columns are supplied in the array Z; if range = ‘V’, the exact value of m is not known in advance and an upper bound must be used. Supplying n columns is always safe.
ldz – [in]
ldz is integer*
The leading dimension of the array Z. ldz >= 1, and if jobz = ‘V’, ldz >= fla_max(1,n).
isuppz – [out]
isuppz is integer array, dimension ( 2*max(1,m))
The support of the eigenvectors in Z, i.e., the indices indicating the nonzero elements in Z. The i-th eigenvector is nonzero only in elements isuppz( 2*i-1) through isuppz( 2*i). This is an output of SSTEMR (tridiagonal matrix). The support of the eigenvectors of A is typically 1:N because of the orthogonal transformations applied by SORMTR.
Implemented only for range = ‘A’ or ‘I’ and iu - il = N - 1WORK – [out]
WORK is DOUBLE PRECISION array, dimension (MAX(1,LWORK))
On exit, if INFO = 0, WORK(1) returns the optimal LWORK.
LWORK – [in]
LWORK is INTEGER
The dimension of the array WORK. LWORK >= fla_max(1,26*N). For optimal efficiency, LWORK >= (NB+6)*N, where NB is the max of the blocksize for DSYTRD and DORMTR returned by ILAENV.
If LWORK = -1, then a workspace query is assumed; the routine only calculates the optimal size of the WORK array, returns this value as the first entry of the WORK array, and no error message related to LWORK is issued by XERBLA.IWORK – [out]
IWORK is INTEGER array, dimension (MAX(1,LIWORK))
On exit, if INFO = 0, IWORK(1) returns the optimal LWORK.
LIWORK – [in]
LIWORK is INTEGER
The dimension of the array IWORK. LIWORK >= fla_max(1,10*N).
If LIWORK = -1, then a workspace query is assumed; the routine only calculates the optimal size of the IWORK array, returns this value as the first entry of the IWORK array, and no error message related to LIWORK is issued by XERBLA.INFO – [out]
INFO is INTEGER
= 0: successful exit
< 0: if INFO = -i, the i-th argument had an illegal value
> 0: Internal error