/* D01TC_T1W_F C++ Header Example Program.
*
* Copyright 2024 Numerical Algorithms Group.
* Mark 30.2, 2024.
*/
#include <dco.hpp>
#include <iostream>
#include <math.h>
#include <nag.h>
#include <nagad.h>
#include <stdio.h>
using namespace std;
int main()
{
// Scalars
int exit_status = 0;
Integer ndim = 4;
cout << "D01TC_T1W_F C++ Header Example Program Results\n\n";
// Allocate memory
Integer * nptvec = 0;
nagad_t1w_w_rtype *abscis = 0, *weight = 0;
Integer lwa = 0;
nptvec = new Integer[ndim];
for (int i = 0; i < ndim; i++)
{
nptvec[i] = 4;
lwa = lwa + nptvec[i];
}
abscis = new nagad_t1w_w_rtype[lwa];
weight = new nagad_t1w_w_rtype[lwa];
// Create AD configuration Data object
Integer ifail = 0;
nag::ad::handle_t ad_handle;
// Evaluate primal weights and abscisae in each Dimension
int j = 0;
for (int i = 0; i < ndim; i++)
{
Integer ifail = 0, quadtype = 0;
nagad_t1w_w_rtype a, b, c, d;
switch (i)
{
case 0:
a = 1.0;
b = 2.0;
c = 0.0;
d = 0.0;
quadtype = 0;
break;
case 1:
a = 0.0;
b = 2.0;
c = 0.0;
d = 0.0;
quadtype = -3;
break;
case 2:
a = 0.0;
b = 0.5;
c = 0.0;
d = 0.0;
quadtype = -4;
break;
case 3:
a = 1.0;
b = 2.0;
c = 0.0;
d = 2.0;
quadtype = -5;
break;
}
nag::ad::d01tc(ad_handle, quadtype, a, b, c, d, nptvec[i], &weight[j],
&abscis[j], ifail);
j = j + nptvec[i];
}
auto fun = [&](nag::ad::handle_t & ad_handle,
const Integer & ndim,
const nagad_t1w_w_rtype *x,
nagad_t1w_w_rtype & ret)
{
// dco/c++ overloading used here to perform AD
double p1 = 6.0, p2 = 8.0;
nagad_t1w_w_rtype r1, r2;
// Split the following function into manageable chunks
// ret = (pow(x[0]*x[1]*x[2],p1)/pow(x[3]+2.0,p2))*
// exp(-2.0*x[1]-0.5*x[2]*x[2]);
r1 = x[2] * x[2];
r1 = 0.5 * r1;
r2 = -2.0 * x[1];
r1 = r2 - r1;
ret = exp(r1);
r1 = x[0] * x[1] * x[2];
r1 = pow(r1, p1);
r2 = x[3] + 2.0;
r2 = pow(r2, p2);
r2 = r1 / r2;
ret = ret * r2;
return;
};
// Call the AD routine
ifail = 0;
nagad_t1w_w_rtype ans;
nag::ad::d01fb(ad_handle, ndim, nptvec, lwa, weight, abscis, fun, ans, ifail);
cout << "\n Derivatives calculated: First order tangents\n";
cout << " Computational mode : algorithmic\n";
// Get derivatives
cout.setf(ios::right);
cout.precision(4);
cout << "\n Solution, x = ";
double ans_value = dco::value(ans);
cout.width(12);
cout << ans_value << endl;
cout << " Derivatives:\n";
cout << " dim j d/dweight d/dabscis\n";
cout.setf(ios::scientific, ios::floatfield);
j = -1;
for (int i = 0; i < ndim; i++)
{
for (int k = 1; k <= nptvec[i]; k++)
{
j = j + 1;
double inc = 1.0, zero = 0.0;
dco::derivative(weight[j]) = inc;
nag::ad::d01fb(ad_handle, ndim, nptvec, lwa, weight, abscis, fun, ans, ifail);
double w = dco::derivative(ans);
dco::derivative(weight[j]) = zero;
dco::derivative(abscis[j]) = inc;
nag::ad::d01fb(ad_handle, ndim, nptvec, lwa, weight, abscis, fun, ans, ifail);
double a = dco::derivative(ans);
dco::derivative(abscis[j]) = zero;
if (k == 1)
{
cout.width(4);
cout << i;
}
else
{
cout << " ";
}
cout.width(8);
cout << k;
cout.width(12);
cout << w;
cout.width(12);
cout << a << endl;
}
}
delete[] nptvec;
delete[] abscis;
delete[] weight;
return exit_status;
}
static void NAG_CALL fun(nag::ad::handle_t & ad_handle,
const Integer & ndim,
const nagad_t1w_w_rtype x[],
nagad_t1w_w_rtype & ret,
Integer iuser[],
nagad_t1w_w_rtype ruser[])
{
// dco/c++ overloading used here to perform AD
double p1 = 6.0, p2 = 8.0;
nagad_t1w_w_rtype r1, r2;
// Split the following function into manageable chunks
// ret = (pow(x[0]*x[1]*x[2],p1)/pow(x[3]+2.0,p2))*
// exp(-2.0*x[1]-0.5*x[2]*x[2]);
r1 = x[2] * x[2];
r1 = 0.5 * r1;
r2 = -2.0 * x[1];
r1 = r2 - r1;
ret = exp(r1);
r1 = x[0] * x[1] * x[2];
r1 = pow(r1, p1);
r2 = x[3] + 2.0;
r2 = pow(r2, p2);
r2 = r1 / r2;
ret = ret * r2;
return;
}