NAG Library Function Document

nag_dbdsdc (f08mdc)

void	nag_dbdsdc (Nag_OrderType order, Nag_UploType uplo, Nag_ComputeSingularVecsType compq, Integer n, double d[], double e[], double u[], Integer pdu, double vt[], Integer pdvt, double q[], Integer iq[], NagError *fail)

3 Description

nag_dbdsdc (f08mdc) computes the singular value decomposition (SVD) of the (upper or lower) bidiagonal matrix

B

B = U S V^{T},

where

S

is a diagonal matrix with non-negative diagonal elements

s_{i i} = s_{i}

, such that

s_{1} \geq s_{2} \geq \dots \geq s_{n} \geq 0,

and

U

and

V

are orthogonal matrices. The diagonal elements of

S

are the singular values of

B

and the columns of

U

and

V

are respectively the corresponding left and right singular vectors of

B

When only singular values are required the function uses the

Q R

algorithm, but when singular vectors are required a divide and conquer method is used. The singular values can optionally be returned in compact form, although currently no function is available to apply

U

V

when stored in compact form.

4 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

Golub G H and Van Loan C F (1996) Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore

5 Arguments

1: order – Nag_OrderTypeInput

On entry: the order argument specifies the two-dimensional storage scheme being used, i.e., row-major ordering or column-major ordering. C language defined storage is specified by

order = Nag_RowMajor

. See Section 3.2.1.3 in the Essential Introduction for a more detailed explanation of the use of this argument.

Constraint:

order = Nag_RowMajor

Nag_ColMajor

2: uplo – Nag_UploTypeInput

On entry: indicates whether

B

is upper or lower bidiagonal.

$uplo = Nag_Upper$: $B$ is upper bidiagonal.
$uplo = Nag_Lower$: $B$ is lower bidiagonal.

Constraint:

uplo = Nag_Upper

Nag_Lower

3: compq – Nag_ComputeSingularVecsTypeInput

On entry: specifies whether singular vectors are to be computed.

$compq = Nag_NotSingularVecs$: Compute singular values only.
$compq = Nag_PackedSingularVecs$: Compute singular values and compute singular vectors in compact form.
$compq = Nag_SingularVecs$: Compute singular values and singular vectors.

Constraint:

compq = Nag_NotSingularVecs

Nag_PackedSingularVecs

Nag_SingularVecs

4: n – IntegerInput

On entry:

n

, the order of the matrix

B

Constraint:

n \geq 0

5: d[ $\dim$ ] – doubleInput/Output

Note: the dimension, dim, of the array d must be at least

\max (1, n)

On entry: the

n

diagonal elements of the bidiagonal matrix

B

On exit: if

fail . code =

NE_NOERROR, the singular values of

B

6: e[ $\dim$ ] – doubleInput/Output

Note: the dimension, dim, of the array e must be at least

\max (1, n - 1)

On entry: the

(n - 1)

off-diagonal elements of the bidiagonal matrix

B

On exit: the contents of e are destroyed.

7: u[ $\dim$ ] – doubleOutput

Note: the dimension, dim, of the array u must be at least

$\max (1, pdu \times n)$ when $compq = Nag_SingularVecs$ ;
$1$ otherwise.

The

(i, j)

th element of the matrix

U

is stored in

$u [(j - 1) \times pdu + i - 1]$ when $order = Nag_ColMajor$ ;
$u [(i - 1) \times pdu + j - 1]$ when $order = Nag_RowMajor$ .

On exit: if

compq = Nag_SingularVecs

, then if

fail . code =

NE_NOERROR, u contains the left singular vectors of the bidiagonal matrix

B

compq \neq Nag_SingularVecs

, u is not referenced.

8: pdu – IntegerInput

On entry: the stride separating row or column elements (depending on the value of order) in the array u.

Constraints:

if $compq = Nag_SingularVecs$ , $pdu \geq \max (1, n)$ ;
otherwise $pdu \geq 1$ .

9: vt[ $\dim$ ] – doubleOutput

Note: the dimension, dim, of the array vt must be at least

$\max (1, pdvt \times n)$ when $compq = Nag_SingularVecs$ ;
$1$ otherwise.

The

(i, j)

th element of the matrix is stored in

$vt [(j - 1) \times pdvt + i - 1]$ when $order = Nag_ColMajor$ ;
$vt [(i - 1) \times pdvt + j - 1]$ when $order = Nag_RowMajor$ .

On exit: if

compq = Nag_SingularVecs

, then if

fail . code =

NE_NOERROR, the rows of vt contain the right singular vectors of the bidiagonal matrix

B

compq \neq Nag_SingularVecs

, vt is not referenced.

10: pdvt – IntegerInput

On entry: the stride separating row or column elements (depending on the value of order) in the array vt.

Constraints:

if $compq = Nag_SingularVecs$ , $pdvt \geq \max (1, n)$ ;
otherwise $pdvt \geq 1$ .

11: q[ $\dim$ ] – doubleOutput

Note: the dimension, dim, of the array q must be at least

\max (1, n^{2} + 5 n, ldq)

On exit: if

compq = Nag_PackedSingularVecs

, then if

fail . code =

NE_NOERROR, q and iq contain the left and right singular vectors in a compact form, requiring

O (n \log_{2} n)

space instead of

2 \times n^{2}

. In particular, q contains all the real data in the first

ldq = n \times (11 + 2 \times smlsiz + 8 \times int (\log_{2} (n / (smlsiz + 1))))

elements of q, where

smlsiz

is equal to the maximum size of the subproblems at the bottom of the computation tree (usually about

25

compq \neq Nag_PackedSingularVecs

, q is not referenced.

12: iq[ $\dim$ ] – IntegerOutput

Note: the dimension, dim, of the array iq must be at least

\max (1, ldiq)

On exit: if

compq = Nag_PackedSingularVecs

, then if

fail . code =

NE_NOERROR, q and iq contain the left and right singular vectors in a compact form, requiring

O (n \log_{2} n)

space instead of

2 \times n^{2}

. In particular, iq contains all integer data in the first

ldiq = n \times (3 + 3 \times int (\log_{2} (n / (smlsiz + 1))))

elements of iq, where

smlsiz

is equal to the maximum size of the subproblems at the bottom of the computation tree (usually about

25

compq \neq Nag_PackedSingularVecs

, iq is not referenced.

13: fail – NagError *Input/Output

The NAG error argument (see Section 3.6 in the Essential Introduction).

6 Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
NE_BAD_PARAM: On entry, argument $⟨value⟩$ had an illegal value.
NE_ENUM_INT_2: On entry, $compq = ⟨value⟩$ , $pdu = ⟨value⟩$ and $n = ⟨value⟩$ .
Constraint: if $compq = Nag_SingularVecs$ , $pdu \geq \max (1, n)$ ;
otherwise $pdu \geq 1$ .
On entry, $compq = ⟨value⟩$ , $pdvt = ⟨value⟩$ and $n = ⟨value⟩$ .
Constraint: if $compq = Nag_SingularVecs$ , $pdvt \geq \max (1, n)$ ;
otherwise $pdvt \geq 1$ .
NE_INT: On entry, $n = ⟨value⟩$ .
Constraint: $n \geq 0$ .
On entry, $pdu = ⟨value⟩$ .
Constraint: $pdu > 0$ .
On entry, $pdvt = ⟨value⟩$ .
Constraint: $pdvt > 0$ .
NE_INTERNAL_ERROR: An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
NE_SINGULAR: The algorithm failed to compute a singular value. The update process of divide-and-conquer failed.

7 Accuracy

Each computed singular value of

B

is accurate to nearly full relative precision, no matter how tiny the singular value. The

i

th computed singular value,

{\hat{s}}_{i}

, satisfies the bound

|{\hat{s}}_{i} - s_{i}| \leq p (n) ε s_{i}

where

ε

is the machine precision and

p (n)

is a modest function of

n

For bounds on the computed singular values, see Section 4.9.1 of Anderson et al. (1999). See also nag_ddisna (f08flc).

8 Parallelism and Performance

nag_dbdsdc (f08mdc) is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

nag_dbdsdc (f08mdc) makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.

Please consult the Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

If only singular values are required, the total number of floating-point operations is approximately proportional to

n^{2}

. When singular vectors are required the number of operations is bounded above by approximately the same number of operations as nag_dbdsqr (f08mec), but for large matrices nag_dbdsdc (f08mdc) is usually much faster.

There is no complex analogue of nag_dbdsdc (f08mdc).

10 Example

This example computes the singular value decomposition of the upper bidiagonal matrix

B = (\begin{array}{r} 3.62 & 1.26 & 0 & 0 \\ 0 & - 2.41 & - 1.53 & 0 \\ 0 & 0 & 1.92 & 1.19 \\ 0 & 0 & 0 & - 1.43 \end{array}) .

NAG Library Function Documentnag_dbdsdc (f08mdc)

+− Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

NAG Library Function Document

nag_dbdsdc (f08mdc)