/* E04DG_A1W_F C++ Header Example Program.
*
* Copyright 2022 Numerical Algorithms Group.
* Mark 28.3, 2022.
*/
#include <dco.hpp>
#include <iostream>
#include <math.h>
#include <nag.h>
#include <nagad.h>
#include <nagx02.h>
#include <stdio.h>
using namespace std;
int main()
{
int exit_status = 0;
nagad_a1w_w_rtype objf;
cout << "E04DG_A1W_F C++ Header Example Program Results\n\n";
// Create AD tape
dco::ga1s<double>::global_tape = dco::ga1s<double>::tape_t::create();
// Create AD configuration data object
Integer ifail = 0;
nag::ad::handle_t ad_handle;
// Read problem parameters and register for differentiation
// Skip first line of data file
string mystr;
getline(cin, mystr);
Integer n;
cin >> n;
// AD routine fixed length array arguments
Integer iwsav[610];
nagad_a1w_w_rtype ruser[1], rwsav[475];
char cwsav[1];
logical lwsav[120];
const Charlen name_l = 6, cwsav_l = 1;
auto objfun = [&](nag::ad::handle_t & ad_handle,
Integer & mode,
const Integer & n,
const nagad_a1w_w_rtype *x,
nagad_a1w_w_rtype & objf,
nagad_a1w_w_rtype *objgrd,
const Integer & nstate)
{
// dco/c++ used here to perform AD of objfun
nagad_a1w_w_rtype x1, x2, y1, y2, expx1;
x1 = x[0];
x2 = x[1];
expx1 = exp(x1);
y1 = 2.0 * x1;
y1 = y1 + x2;
y2 = x2 + 1.0;
x1 = y1 * y1;
x2 = y2 * y2;
x1 = x1 + x2;
objf = expx1 * x1;
if (mode == 2)
{
y2 = y1 + y2;
y2 = 2.0 * y2;
y1 = 4.0 * y1;
y1 = expx1 * y1;
objgrd[0] = y1 + objf;
objgrd[1] = expx1 * y2;
}
};
// AD routine variable length arrays
Integer * iwork = 0;
nagad_a1w_w_rtype *x = 0, *x_in = 0, *objgrd = 0, *work = 0;
if (!(iwork = NAG_ALLOC(n + 1, Integer)) ||
!(x = NAG_ALLOC(n, nagad_a1w_w_rtype)) ||
!(x_in = NAG_ALLOC(n, nagad_a1w_w_rtype)) ||
!(objgrd = NAG_ALLOC(n, nagad_a1w_w_rtype)) ||
!(work = NAG_ALLOC(13 * n, nagad_a1w_w_rtype)))
{
cout << "Allocation failure\n";
exit_status = -1;
goto END;
}
double xr;
for (int i = 0; i < n; i++)
{
cin >> xr;
x_in[i] = xr;
dco::ga1s<double>::global_tape->register_variable(x_in[i]);
x[i] = x_in[i];
}
// Initialize sav arrays
ifail = 0;
nag::ad::e04wb("E04DGA", cwsav, 1, lwsav, 120, iwsav, 610, rwsav, 475, ifail);
// Options can be set here via E04DJA and/or E04DKA
// Solve the problem
Integer iter;
ifail = 0;
nag::ad::e04dg(ad_handle, n, objfun, iter, objf, objgrd, x, iwork, work, lwsav, iwsav, rwsav, ifail);
// Primal results
cout.setf(ios::scientific, ios::floatfield);
cout.precision(3);
cout << "\n Objective value = ";
cout.width(12);
cout << dco::value(objf);
cout << "\n Solution point = ";
for (int i = 0; i < n; i++)
{
cout.width(12);
cout << dco::value(x[i]);
}
cout << "\n Estim gradient = ";
for (int i = 0; i < n; i++)
{
cout.width(12);
cout << dco::value(objgrd[i]);
}
cout << "\n\n Derivatives calculated: First order adjoints\n";
cout << " Computational mode : algorithmic\n\n";
cout << " Derivatives:\n\n";
// Setup evaluation of derivatives of objf via adjoints.
{
double inc = 1.0;
dco::derivative(objf) += inc;
}
ifail = 0;
dco::ga1s<double>::global_tape->sparse_interpret() = true;
dco::ga1s<double>::global_tape->interpret_adjoint();
// Get derivatives of solution points
cout << " dobjf/dx : ";
for (int i = 0; i < n; i++)
{
double d = dco::derivative(x[i]);
cout.width(12);
cout << d;
}
cout << endl;
// Setup evaluation of derivatives via adjoints
for (int j = 0; j < n; j++)
{
dco::ga1s<double>::global_tape->zero_adjoints();
double inc = 1.0;
dco::derivative(objgrd[j]) += inc;
ifail = 0;
dco::ga1s<double>::global_tape->sparse_interpret() = true;
dco::ga1s<double>::global_tape->interpret_adjoint();
cout << " dobjgrd(";
cout.width(1);
cout << j + 1;
cout << ")/dx : ";
for (int i = 0; i < n; i++)
{
double d = dco::derivative(x[i]);
cout.width(12);
cout << d;
}
cout << endl;
}
END:
dco::ga1s<double>::tape_t::remove(dco::ga1s<double>::global_tape);
NAG_FREE(iwork);
NAG_FREE(x);
NAG_FREE(x_in);
NAG_FREE(objgrd);
NAG_FREE(work);
return exit_status;
}