/* E04DG_P0W_F C++ Header Example Program.
 *
 * Copyright 2022 Numerical Algorithms Group.
 * Mark 28.6, 2022.
 */

#include <iostream>
#include <math.h>
#include <nag.h>
#include <nagad.h>
#include <nagx02.h>
#include <stdio.h>
#include <string>
using namespace std;

int main()
{
  int    exit_status = 0;
  double objf;
  cout << "E04DG_P0W_F C++ Header Example Program Results\n\n";

  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;

  // passive routine fixed length array arguments
  Integer       iwsav[610];
  double        ruser[1], rwsav[475];
  char          cwsav[1];
  logical       lwsav[120];
  const Charlen name_l = 6, cwsav_l = 1;

  // passive routine variable length arrays
  Integer *iwork = 0;
  double * x = 0, *x_in = 0, *objgrd = 0, *work = 0;

  iwork  = new Integer[n + 1];
  x      = new double[n];
  x_in   = new double[n];
  objgrd = new double[n];
  work   = new double[13 * n];

  for (int i = 0; i < n; i++)
  {
    cin >> x[i];
  }

  auto objfun = [&](nag::ad::handle_t &     ad_handle,
                  Integer &               mode,
                  const Integer &         n,
                  const double *x,
                  double &     objf,
                  double *objgrd,
                  const Integer &         nstate)
                {
                  // dco/c++ used here to perform AD of objfun
                  double 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;
                  }
                };
  // Initialize sav arrays
  ifail = 0;
  nag::ad::e04wb("E04DGA", cwsav, 1, lwsav, 120, iwsav, 610, rwsav, 475, ifail);

  // 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 << objf;
  cout << "\n Solution point  = ";
  for (int i = 0; i < n; i++)
  {
    cout.width(12);
    cout << x[i];
  }

  cout << "\n Estim gradient  = ";
  for (int i = 0; i < n; i++)
  {
    cout.width(12);
    cout << objgrd[i];
  }
  cout << endl;

  delete[] iwork;
  delete[] x;
  delete[] x_in;
  delete[] objgrd;
  delete[] work;
  return exit_status;
}