f07ca
is the AD Library version of the primal routine
f07caf (dgtsv).
Based (in the C++ interface) on overload resolution,
f07ca can be used for primal, tangent and adjoint
evaluation. It supports tangents and adjoints of first and second 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, dco::gt1s<dco::gt1s<double>::type>::type, dco::ga1s<dco::gt1s<double>::type>::type
Note: this function can be used with AD tools other than dco/c++. For details, please contact NAG.
3Description
f07ca
is the AD Library version of the primal routine
f07caf (dgtsv).
f07caf (dgtsv) computes the solution to a real system of linear equations
where is an tridiagonal matrix and and are matrices.
For further information see Section 3 in the documentation for f07caf (dgtsv).
3.1Symbolic Adjoint
Symbolic strategy may be selected by calling
ad_handle.set_strategy(nag::ad::symbolic)
prior
to calling f07ca. No further
changes are needed compared to using the algorithmic strategy.
3.1.1Mathematical Background
The symbolic adjoint uses the decomposition computed by the primal routine to obtain the adjoint of the right-hand side by solving
(1)
where and denote the th column of the matrices and respectively. The adjoint of the matrix is then computed according to
(2)
where and denote the th column of the matrices and respectively.
You can set or access the adjoints of output argument b. The adjoints of all other output arguments are ignored.
f07ca increments the adjoints of input arguments b, d, du and dl 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
Du Toit J, Naumann U (2017) Adjoint Algorithmic Differentiation Tool Support for Typical Numerical Patterns in Computational Finance
5Arguments
In addition to the arguments present in the interface of the primal routine,
f07ca 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 , .
On exit: any errors are indicated as described in Section 6.
6Error Indicators and Warnings
f07ca uses the standard NAG ifail mechanism. Any errors indicated via info values returned by f07caf may be indicated with the same value returned by ifail. In addition, this routine may return:
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.
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.
A C++ exception was thrown.
The error message will show the details of the C++ exception text.
Dynamic memory allocation failed for AD.
See Error Handling in the NAG AD Library Introduction for further information.
7Accuracy
Not applicable.
8Parallelism and Performance
f07ca
is not threaded in any implementation.
9Further Comments
Since b is not a pure output and there is overwriting of variables, accessing adjoints later may result in wrong values, so a copy of the active input/output is used to obtain correct derivative values. See the example f07ca_a1_algo_dcoe.cpp for details.
10Example
The following examples are variants of the example for
f07caf (dgtsv),
modified to demonstrate calling the NAG AD Library.