naginterfaces.library.lapackeig.zgejsv

naginterfaces.library.lapackeig.zgejsv(joba, jobu, jobv, jobr, jobt, jobp, a)

zgejsv computes the singular value decomposition (SVD) of a real $m\times n$ matrix $A$, where $m\ge n$, and optionally computes the left and/or right singular vectors. zgejsv implements the preconditioned Jacobi SVD of Drmač and Veselić (2008a) and Drmač and Veselić (2008b). This is an expert version of the Jacobi SVD function zgesvj() that employs preprocessing and preconditioning to improve the accuracy of results for extremely ill-conditioned matrices. In most cases zgesvj(), employing fast scaled rotations and de Rijk’s pivoting strategy, is sufficient and is simpler to use. Also consider using zgesvd() or zgesdd() which are much simpler to use and also handle the case $m<n$.

For full information please refer to the NAG Library document for f08kv

https://support.nag.com/numeric/nl/nagdoc_30/flhtml/f08/f08kvf.html

Parameters

jobastr, length 1

Specifies the form of pivoting for the $QR$ factorization stage; whether an estimate of the condition number of the scaled matrix is required; and the form of rank reduction that is performed.

$joba=\text{'C'}$

The initial $QR$ factorization of the input matrix is performed with column pivoting; no estimate of condition number is computed; and, the rank is reduced by only the underflowed part of the triangular factor $R$. This option works well (high relative accuracy) if $A$ can be written in the form $A=BD$, with well-conditioned $B$ and arbitrary diagonal matrix $D$. The accuracy cannot be spoiled by column scaling. The accuracy of the computed output depends on the condition of $B$, and the procedure attempts to achieve the best theoretical accuracy.

$joba=\text{'E'}$

Computation as with $joba=\text{'C'}$ with an additional estimate of the condition number of $B$. It provides a realistic error bound.

$joba=\text{'F'}$

The initial $QR$ factorization of the input matrix is performed with full row and column pivoting; no estimate of condition number is computed; and, the rank is reduced by only the underflowed part of the triangular factor $R$. If $A=D_1\times C\times D_2$ with ill-conditioned diagonal scalings $D_1$, $D_2$, and well-conditioned matrix $C$, this option gives higher accuracy than the $joba=\text{'C'}$ option. If the structure of the input matrix is not known, and relative accuracy is desirable, then this option is advisable.

$joba=\text{'G'}$

Computation as with $joba=\text{'F'}$ with an additional estimate of the condition number of $B$, where $A=DB$ (i.e., $B=C\times D_2$). If $A$ has heavily weighted rows, then using this condition number gives too pessimistic an error bound.

$joba=\text{'A'}$

Computation as with $joba=\text{'C'}$ except in the treatment of rank reduction. In this case, small singular values are to be considered as noise and, if found, the matrix is treated as numerically rank deficient. The computed SVD, $A=U\Sigma V^H$, is such that the relative residual norm (when comparing against $A$) is of the order $O\left(m\right)\times ϵ$, where $ϵ$ is machine precision. This gives the procedure licence to discard (set to zero) all singular values below $n\times ϵ\times \parallel A\parallel$.

$joba=\text{'R'}$

Similar to $joba=\text{'A'}$. The rank revealing property of the initial $QR$ factorization is used to reveal (using the upper triangular factor) a gap, ${\sigma }_{r+1}<ϵ{\sigma }_r$, in which case the numerical rank is declared to be $r$. The SVD is computed with absolute error bounds, but more accurately than with $joba=\text{'A'}$.

jobustr, length 1

Specifies options for computing the left singular vectors $U$.

$jobu=\text{'U'}$

The first $n$ left singular vectors (columns of $U$) are computed and returned in the array $u$.

$jobu=\text{'F'}$

All $m$ left singular vectors are computed and returned in the array $u$.

$jobu=\text{'W'}$

No left singular vectors are computed, but the array $u$ (with $\text{ldu}\ge m$ and second dimension at least $\text{n}$) is available as workspace for computing right singular values. See the description of $u$.

$jobu=\text{'N'}$

No left singular vectors are computed. $u$ is not referenced when $jobv=\text{'W'}$ or $\text{'N'}$.

jobvstr, length 1

Specifies options for computing the right singular vectors $V$.

$jobv=\text{'V'}$

The $n$ right singular vectors (columns of $V$) are computed and returned in the array $v$; Jacobi rotations are not explicitly accumulated.

$jobv=\text{'J'}$

The $n$ right singular vectors (columns of $V$) are computed and returned in the array $v$, but they are computed as the product of Jacobi rotations. This option is allowed only if $jobu=\text{'U'}$ or $\text{'F'}$, i.e., in computing the full SVD.

This is equivalent to multiplying the input matrix, on the right, by the matrix $V$.

$jobv=\text{'W'}$

No right singular values are computed, but the array $v$ (with $\text{ldv}\ge n$ and second dimension at least $\text{n}$) is available as workspace for computing left singular values. See the description of $v$.

$jobv=\text{'N'}$

No right singular vectors are computed. $v$ is not referenced when $jobu=\text{'W'}$ or $\text{'N'}$ or $jobt=\text{'N'}$ or $m\ne n$.

jobrstr, length 1

Specifies the conditions under which columns of $A$ are to be set to zero. This effectively specifies a lower limit on the range of singular values; any singular values below this limit are (through column zeroing) set to zero. If $A\ne 0$ is scaled so that the largest column (in the Euclidean norm) of $cA$ is equal to the square root of the overflow threshold, then $jobr$ allows the function to kill columns of $A$ whose norm in $cA$ is less than $\sqrt{\text{sfmin}}$ (for $jobr=\text{'R'}$), or less than $\text{sfmin}/ϵ$ (otherwise). $\text{sfmin}$ is the safe range parameter, as returned by function machine.real_safe.

$jobr=\text{'N'}$

Only set to zero those columns of $A$ for which the norm of corresponding column of $cA<\text{sfmin}/ϵ$, that is, those columns that are effectively zero (to machine precision) anyway. If the condition number of $A$ is greater than the overflow threshold $\lambda$, where $\lambda$ is the value returned by machine.real_largest, you are recommended to use function zgesvj().

$jobr=\text{'R'}$

Set to zero those columns of $A$ for which the norm of the corresponding column of $cA<\sqrt{\text{sfmin}}$. This approximately represents a restricted range for $\sigma \left(cA\right)$ of $[\sqrt{\text{sfmin}},\sqrt{\lambda }]$.

For computing the singular values in the full range from the safe minimum up to the overflow threshold use zgesvj().

Suggested value: $jobr=\text{'R'}$.

jobtstr, length 1

Specifies, in the case $n=m$, whether the function is permitted to use the conjugate transpose of $A$ for improved efficiency. If the matrix is square, then the procedure may use $A^H$ if it seems to be better with respect to convergence. If the matrix is not square, $jobt$ is ignored. The decision is based on two values of entropy over the adjoint orbit of $A^{H}A$. See the descriptions of $rworkb\left[5\right]$ and $rworkb\left[6\right]$.

$jobt=\text{'T'}$

If $n=m$, perform an entropy test and, if the test indicates possibly faster convergence of the Jacobi process when using $A^H$, then form the conjugate transpose $A^H$. If $A$ is replaced with $A^H$, then the row pivoting is included automatically.

$jobt=\text{'N'}$

No entropy test and no transposition is performed.

The option $jobt=\text{'T'}$ can be used to compute only the singular values, or the full SVD ($U$, $\Sigma$ and $V$).

In the case where only one set of singular vectors ($U$ or $V$) is required, the caller must still provide both $u$ and $v$, as one of the matrices is used as workspace if the matrix $A$ is transposed.

See the descriptions of $u$ and $v$.

jobpstr, length 1

Specifies whether the function should be allowed to introduce structured perturbations to drown denormalized numbers. For details see Drmač and Veselić (2008a) and Drmač and Veselić (2008b). For the sake of simplicity, these perturbations are included only when the full SVD or only the singular values are requested.

$jobp=\text{'P'}$

Introduce perturbation if $A$ is found to be very badly scaled (introducing denormalized numbers).

$jobp=\text{'N'}$

Do not perturb.

acomplex, array-like, shape $(m,n)$

The $m\times n$ matrix $A$.

Returns

acomplex, ndarray, shape $(m,n)$

The contents of $a$ are overwritten.

svafloat, ndarray, shape $\left(n\right)$

The, possibly scaled, singular values of $A$.

The singular values of $A$ are ${\sigma }_{\text{i}}=\alpha \times sva[\text{i}-1]$, for $\text{i}=1,2,\dots ,n$, where $\alpha =rworkb\left[0\right]/rworkb\left[1\right]$.

Normally $\alpha =1$ and no scaling is required to obtain the singular values.

However, if the largest singular value of $A$ overflows or if small singular values have been saved from underflow by scaling the input matrix $A$, then $\alpha \ne 1$.

If $jobr=\text{'R'}$, then some of the singular values may be returned as exact zeros because they are below the numerical rank threshold or are denormalized numbers.

ucomplex, ndarray, shape $(:,:)$

If $jobu=\text{'U'}$, $u$ contains the $m\times n$ matrix of left singular vectors.

If $jobu=\text{'F'}$, $u$ contains the $m\times m$ matrix of left singular vectors, including an orthonormal basis of the orthogonal complement of Range($A$).

$u$ is not referenced when $jobu=\text{'W'}$ or $\text{'N'}$ and one of the following is satisfied:

$jobv=\text{'W'}$ or $\text{'N'}$, or

$n=1$, or

$A$ is the zero matrix.

vcomplex, ndarray, shape $(:,:)$

If $jobv=\text{'V'}$ or $\text{'J'}$, $v$ contains the $n\times n$ matrix of right singular vectors.

$v$ is not referenced when $jobv=\text{'W'}$ or $\text{'N'}$ and one of the following is satisfied:

$jobu=\text{'U'}$ or $\text{'F'}$ and $jobt=\text{'T'}$, or

$n=1$, or

$A$ is the zero matrix.

rworkbfloat, ndarray, shape $\left(7\right)$

Contains information about the completed job.

$rworkb\left[0\right]$

$\alpha =rworkb\left[0\right]/rworkb\left[1\right]$ is the scaling factor such that ${\sigma }_{\text{i}}=\alpha \times sva[\text{i}-1]$, for $\text{i}=1,2,\dots ,n$, are the computed singular values of $A$. See the description of $sva$.

$rworkb\left[1\right]$

See the description of $rworkb\left[0\right]$.

$rworkb\left[2\right]$

$\text{sconda}$, an estimate for the condition number of column equilibrated $A$ (if $joba=\text{'E'}$ or $\text{'G'}$). $\text{sconda}$ is an estimate of $\sqrt{\left({\Vert {\left(R^{T}R\right)}^{-1}\Vert }_1\right)}$. It is computed using lapacklin.dpocon. It satisfies $n^{-\frac{1}{4}}\times \text{sconda}\le {\Vert R^{-1}\Vert }_2\le n^{\frac{1}{4}}\times \text{sconda}$ where $R$ is the triangular factor from the $QR$ factorization of $A$. However, if $R$ is truncated and the numerical rank is determined to be strictly smaller than $n$, $\text{sconda}$ is returned as $-1$, thus indicating that the smallest singular values might be lost.

If the full SVD is needed, and you are familiar with the details of the method, the following two condition numbers are useful for the analysis of the algorithm.

$rworkb\left[3\right]$

An estimate of the scaled condition number of the triangular factor in the first $QR$ factorization.

$rworkb\left[4\right]$

An estimate of the scaled condition number of the triangular factor in the second $QR$ factorization.

The following two parameters are computed if $jobt=\text{'T'}$.

$rworkb\left[5\right]$

The entropy of $A^{T}A$: this is the Shannon entropy of $diag\left(A^{T}A\right)/trace\left(A^{T}A\right)$ taken as a point in the probability simplex.

$rworkb\left[6\right]$

The entropy of $A{A}^T$.

iworkbNone or int, ndarray, shape $(:)$

Contains information about the completed job.

$iworkb\left[0\right]$

The numerical rank of $A$ determined after the initial $QR$ factorization with pivoting. See the descriptions of $joba$ and $jobr$.

$iworkb\left[1\right]$

The number of computed nonzero singular values.

$iworkb\left[2\right]$

If nonzero, a warning message. If $iworkb\left[2\right]=1$, then some of the column norms of $A$ were denormalized (tiny) numbers. The requested high accuracy is not warranted by the data.

Raises

NagValueError

(errno $-1$)

On entry, error in parameter $joba$.

Constraint: $joba=\text{'C'}$, $\text{'E'}$, $\text{'F'}$, $\text{'G'}$, $\text{'A'}$ or $\text{'R'}$.

(errno $-2$)

On entry, error in parameter $jobu$.

Constraint: $jobu=\text{'U'}$, $\text{'F'}$, $\text{'W'}$ or $\text{'N'}$.

(errno $-3$)

On entry, error in parameter $jobv$.

Constraint: $jobv=\text{'V'}$, $\text{'J'}$, $\text{'W'}$ or $\text{'N'}$.

(errno $-3$)

On entry, error in parameter $jobv$.

Constraint: $jobv\ne \text{'J'}$.

(errno $-4$)

On entry, error in parameter $jobr$.

Constraint: $jobr=\text{'N'}$ or $\text{'R'}$.

(errno $-5$)

On entry, error in parameter $jobt$.

Constraint: $jobt=\text{'T'}$ or $\text{'N'}$.

(errno $-6$)

On entry, error in parameter $jobp$.

Constraint: $jobp=\text{'P'}$ or $\text{'N'}$.

(errno $-7$)

On entry, error in parameter $\text{m}$.

Constraint: $m\ge 0$.

(errno $-8$)

On entry, error in parameter $\text{n}$.

Constraint: $m\ge n\ge 0$.

Warns

NagAlgorithmicWarning

(errno $i>0$)

zgejsv did not converge in the allowed number of iterations ($30$). The computed values might be inaccurate.

Notes

The SVD is written as

$$A=U\Sigma V^H\text{,}$$

where $\Sigma$ is an $m\times n$ matrix which is zero except for its $n$ diagonal elements, $U$ is an $m\times m$ unitary matrix, and $V$ is an $n\times n$ unitary matrix. The diagonal elements of $\Sigma$ are the singular values of $A$ in descending order of magnitude. The columns of $U$ and $V$ are the left and the right singular vectors of $A$, respectively.

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, https://www.netlib.org/lapack/lug

Drmač, Z and Veselić, K, 2008, New fast and accurate Jacobi SVD Algorithm I, SIAM J. Matrix Anal. Appl. (29 4)

Drmač, Z and Veselić, K, 2008, New fast and accurate Jacobi SVD Algorithm II, SIAM J. Matrix Anal. Appl. (29 4)

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