f08kd
is the AD Library version of the primal routine
f08kdf (dgesdd).
Based (in the C++ interface) on overload resolution,
f08kd can be used for primal, tangent and adjoint
evaluation. It supports tangents and adjoints of first order.
The parameter ad_handle can be used to choose whether adjoints are computed using a symbolic adjoint or straightforward algorithmic differentiation.
Corresponding to the overloaded C++ function, the Fortran interface provides five routines with names reflecting the type used for active real arguments. The actual subroutine and type names are formed by replacing AD and ADTYPE in the above as follows:
The function is overloaded on ADTYPE which represents the type of active arguments. ADTYPE may be any of the following types: double, dco::ga1s<double>::type, dco::gt1s<double>::type
Note: this function can be used with AD tools other than dco/c++. For details, please contact NAG.
3Description
f08kd
is the AD Library version of the primal routine
f08kdf (dgesdd).
f08kdf (dgesdd) computes the singular value decomposition (SVD) of a real $m\times n$ matrix $A$, optionally computing the left and/or right singular vectors, by using a divide-and-conquer method.
For further information see Section 3 in the documentation for f08kdf (dgesdd).
3.1Symbolic Adjoint
f08kd can provide symbolic adjoints by setting the symbolic strategy as described in Section 3.3.3 in the Introduction to the NAG AD Library.
The symbolic adjoint allows you to compute the adjoints of the output arguments:
The symbolic adjoint assumes that the primal routine has successfully converged. Moreover for considering the adjoints of s the first $\mathrm{min}(m,n)$ columns of u and the first $\mathrm{min}(m,n)$ rows of vt are required. To consider the adjoints of the first $\mathrm{min}(m,n)$ columns of u and/or the first $\mathrm{min}(m,n)$ rows of vt the algorithm requires the computation of all entries of the matrices $U$ and $V$.
Hence (to compute the desired adjoint) if the routine is run with ${\mathbf{jobz}}=\text{'N'}$ the SVD decomposition is performed by calling f08kd with ${\mathbf{jobz}}=\text{'S'}$ (you must ensure that all arrays are allocated as specified for ${\mathbf{jobz}}=\text{'S'}$). The results are stored according to the value jobz you provided.
For all other settings of jobz the SVD decomposition is performed by calling the f08kdf with ${\mathbf{jobz}}=\text{'A'}$ (you must ensure that all arrays are allocated as specified for ${\mathbf{jobz}}=\text{'A'}$). The results are stored according to the value jobz you provided.
3.1.1Mathematical Background
The symbolic adjoint uses the SVD decomposition computed by the primal routine to obtain the adjoints. To compute the adjoints it is required that
(i)${\sigma}_{i}\ne {\sigma}_{j}$ for all $i\ne j$, $1\le i,j\le \mathrm{min}(m,n)$;
(ii)if $m\ne n$ then ${\sigma}_{i}\ne 0$ for all $1\le i\le \mathrm{min}(m,n)$,
where ${\sigma}_{i}$ denotes the $i$th singular value of matrix $A$. Please see Giles (2017) for more details.
3.1.2Usable adjoints
You can set or access the adjoints of the output arguments a if ${\mathbf{jobz}}=\text{'O'}$, s, u if ${\mathbf{jobz}}\ne \text{'O'}$ and $m\ge n$, and vt if ${\mathbf{jobz}}\ne \text{'O'}$ and $m<n$. The adjoints of all other output arguments are ignored.
f08kd increments the adjoints of input argument a according to the first order adjoint model.
4References
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
Giles M (2017) Collected Matrix Derivative Results for Forward and Reverse Mode Algorithmic Differentiation
Golub G H and Van Loan C F (1996) Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore
5Arguments
In addition to the arguments present in the interface of the primal routine,
f08kd includes some arguments specific to AD.
A brief summary of the AD specific arguments is given below. For the remainder, links are provided to the corresponding argument from the primal routine.
A tooltip popup for all arguments can be found by hovering over the argument name in Section 2 and in this section.
On entry: a configuration object that holds information on the differentiation strategy. Details on setting the AD strategy are described in AD handle object and AD Strategies in the NAG AD Library Introduction.
On entry: must be set to $0$, $-1\text{\hspace{0.25em}or\hspace{0.25em}}1$.
On exit: any errors are indicated as described in Section 6.
6Error Indicators and Warnings
f08kd uses the standard NAG ifail mechanism. Any errors indicated via info values returned by f08kdf may be indicated with the same value returned by ifail. In addition, this routine may return:
${\mathbf{ifail}}=-89$
An unexpected AD error has been triggered by this routine. Please
contact NAG.
See Error Handling in the NAG AD Library Introduction for further information.
${\mathbf{ifail}}=-199$
The routine was called using a strategy that has not yet been implemented.
See AD Strategies in the NAG AD Library Introduction for further information.
${\mathbf{ifail}}=-444$
A C++ exception was thrown.
The error message will show the details of the C++ exception text.
${\mathbf{ifail}}=-899$
Dynamic memory allocation failed for AD.
See Error Handling in the NAG AD Library Introduction for further information.
If the symbolic strategy is used, the following may be returned:
${\mathbf{ifail}}=10$
Singular values are not distinct.
${\mathbf{ifail}}=11$
At least one singular value is numerically zero.
7Accuracy
Not applicable.
8Parallelism and Performance
f08kd
is not threaded in any implementation.
9Further Comments
None.
10Example
The following examples are variants of the example for
f08kdf (dgesdd),
modified to demonstrate calling the NAG AD Library.
Description of the primal example.
This example finds the singular values and left and right singular vectors of the $4\times 6$ matrix