NAG AD Library
d02pf (ivp_rkts_onestep)

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1 Purpose

d02pf is the AD Library version of the primal routine d02pff. Based (in the C++ interface) on overload resolution, d02pf can be used for primal, tangent and adjoint evaluation. It supports tangents and adjoints of first and second order.

2 Specification

C++ Interface
#include <dco.hpp>
#include <nagad.h>
namespace nag {
namespace ad {
template <typename F_T>
void d02pf ( handle_t &ad_handle, F_T &&f, const Integer &n, ADTYPE &tnow, ADTYPE ynow[], ADTYPE ypnow[], Integer iwsav[], ADTYPE rwsav[], Integer &ifail)
}
}
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.

3 Description

d02pf is the AD Library version of the primal routine d02pff.
d02pff is a one-step routine for solving an initial value problem for a first-order system of ordinary differential equations using Runge–Kutta methods. For further information see Section 3 in the documentation for d02pff.

4 References

Brankin R W, Gladwell I and Shampine L F (1991) RKSUITE: A suite of Runge–Kutta codes for the initial value problems for ODEs SoftReport 91-S1 Southern Methodist University

5 Arguments

In addition to the arguments present in the interface of the primal routine, d02pf 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.
1: ad_handlenag::ad::handle_t Input/Output
On entry: a configuration object that holds information on the differentiation strategy. Details on setting the AD strategy are described in AD handle object in the NAG AD Library Introduction.
2: f – Callable Input
f needs to be callable with the specification listed below. This can be a C++ lambda, a functor or a (static member) function pointer. If using a lambda, parameters can be captured safely by reference. No copies of the callable are made internally.
The specification of f is:
C++ Interface
auto f = [&]( const handle_t &ad_handle, const ADTYPE &t, const Integer &n, const ADTYPE y[], ADTYPE yp[])
1: ad_handlenag::ad::handle_t Input/Output
On entry: a handle to the AD handle object.
2: tADTYPE Input
3: n – Integer Input
4: yADTYPE array Input
5: ypADTYPE array Output
3: n – Integer Input
4: tnowADTYPE Output
5: ynow(n) – ADTYPE array Output
6: ypnow(n) – ADTYPE array Output
7: iwsav(130) – Integer array Communication Array
8: rwsav(32×n+350) – ADTYPE array Communication Array
Please consult Overwriting of Inputs in the NAG AD Library Introduction.
9: ifail – Integer Input/Output

6 Error Indicators and Warnings

d02pf preserves all error codes from d02pff and in addition can return:
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.
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.
ifail=-444
A C++ exception was thrown.
The error message will show the details of the C++ exception text.
ifail=-899
Dynamic memory allocation failed for AD.
See Error Handling in the NAG AD Library Introduction for further information.

7 Accuracy

Not applicable.

8 Parallelism and Performance

d02pf is not threaded in any implementation.

9 Further Comments

None.

10 Example

The following examples are variants of the example for d02pff, modified to demonstrate calling the NAG AD Library.
Description of the primal example.
This example solves the equation
y = -y ,   y(0) = 0 ,   y(0) = 1  
reposed as
y1 = y2  
y2 = -y1  
over the range [0,2π] with initial conditions y1 = 0.0 and y2 = 1.0 . We use relative error control with threshold values of 1.0E−8 for each solution component and print the solution at each integration step across the range. We use a medium order Runge–Kutta method (method=2) with tolerances tol=1.0E−4 and tol=1.0E−5 in turn so that we may compare the solutions.

10.1 Adjoint modes

Language Source File Data Results
C++ d02pf_a1_algo_dcoe.cpp None d02pf_a1_algo_dcoe.r
C++ d02pf_a1t1_algo_dcoe.cpp None d02pf_a1t1_algo_dcoe.r

10.2 Tangent modes

Language Source File Data Results
C++ d02pf_t1_algo_dcoe.cpp None d02pf_t1_algo_dcoe.r
C++ d02pf_t2_algo_dcoe.cpp None d02pf_t2_algo_dcoe.r

10.3 Passive mode

Language Source File Data Results
C++ d02pf_passive_dcoe.cpp None d02pf_passive_dcoe.r