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Chapter Contents
Chapter Introduction
NAG Toolbox

# NAG Toolbox: nag_lapack_dsbgvd (f08uc)

## Purpose

nag_lapack_dsbgvd (f08uc) computes all the eigenvalues and, optionally, the eigenvectors of a real generalized symmetric-definite banded eigenproblem, of the form
 Az = λBz , $Az=λBz ,$
where A$A$ and B$B$ are symmetric and banded, and B$B$ is also positive definite. If eigenvectors are desired, it uses a divide-and-conquer algorithm.

## Syntax

[ab, bb, w, z, info] = f08uc(jobz, uplo, ka, kb, ab, bb, 'n', n)
[ab, bb, w, z, info] = nag_lapack_dsbgvd(jobz, uplo, ka, kb, ab, bb, 'n', n)

## Description

The generalized symmetric-definite band problem
 Az = λ Bz $Az = λ Bz$
is first reduced to a standard band symmetric problem
 Cx = λx , $Cx = λx ,$
where C$C$ is a symmetric band matrix, using Wilkinson's modification to Crawford's algorithm (see Crawford (1973) and Wilkinson (1977)). The symmetric eigenvalue problem is then solved for the eigenvalues and the eigenvectors, if required, which are then backtransformed to the eigenvectors of the original problem.
The eigenvectors are normalized so that the matrix of eigenvectors, Z$Z$, satisfies
 ZT A Z = Λ   and   ZT B Z = I , $ZT A Z = Λ and ZT B Z = I ,$
where Λ $\Lambda$ is the diagonal matrix whose diagonal elements are the eigenvalues.

## References

Anderson E, Bai Z, Bischof C, Blackford S, Demmel J, Dongarra J J, Du Croz J J, Greenbaum A, Hammarling S, McKenney A and Sorensen D (1999) LAPACK Users' Guide (3rd Edition) SIAM, Philadelphia http://www.netlib.org/lapack/lug
Crawford C R (1973) Reduction of a band-symmetric generalized eigenvalue problem Comm. ACM 16 41–44
Golub G H and Van Loan C F (1996) Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore
Wilkinson J H (1977) Some recent advances in numerical linear algebra The State of the Art in Numerical Analysis (ed D A H Jacobs) Academic Press

## Parameters

### Compulsory Input Parameters

1:     jobz – string (length ≥ 1)
Indicates whether eigenvectors are computed.
jobz = 'N'${\mathbf{jobz}}=\text{'N'}$
Only eigenvalues are computed.
jobz = 'V'${\mathbf{jobz}}=\text{'V'}$
Eigenvalues and eigenvectors are computed.
Constraint: jobz = 'N'${\mathbf{jobz}}=\text{'N'}$ or 'V'$\text{'V'}$.
2:     uplo – string (length ≥ 1)
If uplo = 'U'${\mathbf{uplo}}=\text{'U'}$, the upper triangles of A$A$ and B$B$ are stored.
If uplo = 'L'${\mathbf{uplo}}=\text{'L'}$, the lower triangles of A$A$ and B$B$ are stored.
Constraint: uplo = 'U'${\mathbf{uplo}}=\text{'U'}$ or 'L'$\text{'L'}$.
3:     ka – int64int32nag_int scalar
If uplo = 'U'${\mathbf{uplo}}=\text{'U'}$, the number of superdiagonals, ka${k}_{a}$, of the matrix A$A$.
If uplo = 'L'${\mathbf{uplo}}=\text{'L'}$, the number of subdiagonals, ka${k}_{a}$, of the matrix A$A$.
Constraint: ka0${\mathbf{ka}}\ge 0$.
4:     kb – int64int32nag_int scalar
If uplo = 'U'${\mathbf{uplo}}=\text{'U'}$, the number of superdiagonals, kb${k}_{b}$, of the matrix B$B$.
If uplo = 'L'${\mathbf{uplo}}=\text{'L'}$, the number of subdiagonals, kb${k}_{b}$, of the matrix B$B$.
Constraint: kakb0${\mathbf{ka}}\ge {\mathbf{kb}}\ge 0$.
5:     ab(ldab, : $:$) – double array
The first dimension of the array ab must be at least ka + 1${\mathbf{ka}}+1$
The second dimension of the array must be at least max (1,n)$\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$
The upper or lower triangle of the n$n$ by n$n$ symmetric band matrix A$A$.
The matrix is stored in rows 1$1$ to ka + 1${k}_{a}+1$, more precisely,
• if uplo = 'U'${\mathbf{uplo}}=\text{'U'}$, the elements of the upper triangle of A$A$ within the band must be stored with element Aij${A}_{ij}$ in ab(ka + 1 + ij,j)​ for ​max (1,jka)ij${\mathbf{ab}}\left({k}_{a}+1+i-j,j\right)\text{​ for ​}\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,j-{k}_{a}\right)\le i\le j$;
• if uplo = 'L'${\mathbf{uplo}}=\text{'L'}$, the elements of the lower triangle of A$A$ within the band must be stored with element Aij${A}_{ij}$ in ab(1 + ij,j)​ for ​jimin (n,j + ka).${\mathbf{ab}}\left(1+i-j,j\right)\text{​ for ​}j\le i\le \mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(n,j+{k}_{a}\right)\text{.}$
6:     bb(ldbb, : $:$) – double array
The first dimension of the array bb must be at least kb + 1${\mathbf{kb}}+1$
The second dimension of the array must be at least max (1,n)$\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$
The upper or lower triangle of the n$n$ by n$n$ symmetric band matrix B$B$.
The matrix is stored in rows 1$1$ to kb + 1${k}_{b}+1$, more precisely,
• if uplo = 'U'${\mathbf{uplo}}=\text{'U'}$, the elements of the upper triangle of B$B$ within the band must be stored with element Bij${B}_{ij}$ in bb(kb + 1 + ij,j)​ for ​max (1,jkb)ij${\mathbf{bb}}\left({k}_{b}+1+i-j,j\right)\text{​ for ​}\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,j-{k}_{b}\right)\le i\le j$;
• if uplo = 'L'${\mathbf{uplo}}=\text{'L'}$, the elements of the lower triangle of B$B$ within the band must be stored with element Bij${B}_{ij}$ in bb(1 + ij,j)​ for ​jimin (n,j + kb).${\mathbf{bb}}\left(1+i-j,j\right)\text{​ for ​}j\le i\le \mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(n,j+{k}_{b}\right)\text{.}$

### Optional Input Parameters

1:     n – int64int32nag_int scalar
Default: The second dimension of the arrays ab, bb. (An error is raised if these dimensions are not equal.)
n$n$, the order of the matrices A$A$ and B$B$.
Constraint: n0${\mathbf{n}}\ge 0$.

### Input Parameters Omitted from the MATLAB Interface

ldab ldbb ldz work lwork iwork liwork

### Output Parameters

1:     ab(ldab, : $:$) – double array
The first dimension of the array ab will be ka + 1${\mathbf{ka}}+1$
The second dimension of the array will be max (1,n)$\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$
ldabka + 1$\mathit{ldab}\ge {\mathbf{ka}}+1$.
The contents of ab are overwritten.
2:     bb(ldbb, : $:$) – double array
The first dimension of the array bb will be kb + 1${\mathbf{kb}}+1$
The second dimension of the array will be max (1,n)$\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$
ldbbkb + 1$\mathit{ldbb}\ge {\mathbf{kb}}+1$.
The factor S$S$ from the split Cholesky factorization B = STS$B={S}^{\mathrm{T}}S$, as returned by nag_lapack_dpbstf (f08uf).
3:     w(n) – double array
The eigenvalues in ascending order.
4:     z(ldz, : $:$) – double array
The first dimension, ldz, of the array z will be
• if jobz = 'V'${\mathbf{jobz}}=\text{'V'}$, ldz max (1,n) $\mathit{ldz}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$;
• otherwise ldz1$\mathit{ldz}\ge 1$.
The second dimension of the array will be max (1,n)$\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$ if jobz = 'V'${\mathbf{jobz}}=\text{'V'}$, and at least 1$1$ otherwise
If jobz = 'V'${\mathbf{jobz}}=\text{'V'}$, z contains the matrix Z$Z$ of eigenvectors, with the i$i$th column of Z$Z$ holding the eigenvector associated with w(i)${\mathbf{w}}\left(i\right)$. The eigenvectors are normalized so that ZTBZ = I${Z}^{\mathrm{T}}BZ=I$.
If jobz = 'N'${\mathbf{jobz}}=\text{'N'}$, z is not referenced.
5:     info – int64int32nag_int scalar
info = 0${\mathbf{info}}=0$ unless the function detects an error (see Section [Error Indicators and Warnings]).

## Error Indicators and Warnings

info = i${\mathbf{info}}=-i$
If info = i${\mathbf{info}}=-i$, parameter i$i$ had an illegal value on entry. The parameters are numbered as follows:
1: jobz, 2: uplo, 3: n, 4: ka, 5: kb, 6: ab, 7: ldab, 8: bb, 9: ldbb, 10: w, 11: z, 12: ldz, 13: work, 14: lwork, 15: iwork, 16: liwork, 17: info.
It is possible that info refers to a parameter that is omitted from the MATLAB interface. This usually indicates that an error in one of the other input parameters has caused an incorrect value to be inferred.
INFO > 0${\mathbf{INFO}}>0$
If info = i${\mathbf{info}}=i$ and in$i\le {\mathbf{n}}$, the algorithm failed to converge; i$i$ off-diagonal elements of an intermediate tridiagonal form did not converge to zero.
If info = i${\mathbf{info}}=i$ and i > n$i>{\mathbf{n}}$, if info = n + i${\mathbf{info}}={\mathbf{n}}+i$, for 1in$1\le i\le {\mathbf{n}}$, then nag_lapack_dpbstf (f08uf) returned info = i${\mathbf{info}}=i$: B$B$ is not positive definite. The factorization of B$B$ could not be completed and no eigenvalues or eigenvectors were computed.

## Accuracy

If B$B$ is ill-conditioned with respect to inversion, then the error bounds for the computed eigenvalues and vectors may be large, although when the diagonal elements of B$B$ differ widely in magnitude the eigenvalues and eigenvectors may be less sensitive than the condition of B$B$ would suggest. See Section 4.10 of Anderson et al. (1999) for details of the error bounds.

The total number of floating point operations is proportional to n3 ${n}^{3}$ if jobz = 'V'${\mathbf{jobz}}=\text{'V'}$ and, assuming that nka $n\gg {k}_{a}$, is approximately proportional to n2 ka ${n}^{2}{k}_{a}$ otherwise.
The complex analogue of this function is nag_lapack_zhbgvd (f08uq).

## Example

```function nag_lapack_dsbgvd_example
jobz = 'No vectors';
uplo = 'U';
ka = int64(2);
kb = int64(1);
ab = [0, 0, 0.42, 0.63;
0, 0.39, 0.79, 0.48;
0.24, -0.11, -0.25, -0.03];
bb = [0, 0.95, -0.29, -0.33;
2.07, 1.69, 0.65, 1.17];
[abOut, bbOut, w, z, info] = nag_lapack_dsbgvd(jobz, uplo, ka, kb, ab, bb)
```
```

0         0    0.3912    0.9434
0    0.5117    0.9991   -0.0630
0.1159    1.1546    0.0265   -0.8160

bbOut =

0    0.6603   -0.3886   -0.3051
1.4387    1.0502    0.7463    1.0817

w =

-0.8305
-0.6401
0.0992
1.8525

z =

0

info =

0

```
```function f08uc_example
jobz = 'No vectors';
uplo = 'U';
ka = int64(2);
kb = int64(1);
ab = [0, 0, 0.42, 0.63;
0, 0.39, 0.79, 0.48;
0.24, -0.11, -0.25, -0.03];
bb = [0, 0.95, -0.29, -0.33;
2.07, 1.69, 0.65, 1.17];
[abOut, bbOut, w, z, info] = f08uc(jobz, uplo, ka, kb, ab, bb)
```
```

0         0    0.3912    0.9434
0    0.5117    0.9991   -0.0630
0.1159    1.1546    0.0265   -0.8160

bbOut =

0    0.6603   -0.3886   -0.3051
1.4387    1.0502    0.7463    1.0817

w =

-0.8305
-0.6401
0.0992
1.8525

z =

0

info =

0

```