/* nag_correg_corrmat_target (g02apc) Example Program.
 *
 * Copyright 2023 Numerical Algorithms Group.
 *
 * Mark 29.1, 2023.
 */

#include <nag.h>
#include <stdio.h>

int main(void) {

#define G(I, J) g[(J - 1) * pdg + I - 1]
#define H(I, J) h[(J - 1) * pdh + I - 1]

  /*  Scalars */
  Integer exit_status = 0;
  Integer one = 1;
  double alpha, eigmin, eigtol, errtol, norm, theta;
  Integer i, j, iter, n, pdg, pdh, pdx;

  /*  Arrays */
  double *eig = 0, *g = 0, *h = 0, *x = 0;

  /* Nag Types */
  Nag_OrderType order;
  NagError fail;

  INIT_FAIL(fail);

  /* Output preamble */
  printf("nag_correg_corrmat_target (g02apc)");
  printf(" Example Program Results\n\n");
  fflush(stdout);

  /* Skip heading in data file */
  scanf("%*[^\n] ");

  /* Read in the problem size and theta */
  scanf("%" NAG_IFMT "%lf%*[^\n] ", &n, &theta);

  pdg = n;
  pdh = n;
  pdx = n;
  if (!(eig = NAG_ALLOC(n, double)) || !(g = NAG_ALLOC(pdg * n, double)) ||
      !(h = NAG_ALLOC(pdh * n, double)) || !(x = NAG_ALLOC(pdx * n, double))) {
    printf("Allocation failure\n");
    exit_status = -1;
    goto END;
  }

  /* Read in the matrix g */
  for (i = 1; i <= n; i++)
    for (j = 1; j <= n; j++)
      scanf("%lf", &G(i, j));
  scanf("%*[^\n] ");

  /* Read in the matrix h */
  for (i = 1; i <= n; i++)
    for (j = 1; j <= n; j++)
      scanf("%lf", &H(i, j));
  scanf("%*[^\n] ");

  /* Use the defaults for ERRTOL and EIGTOL */
  errtol = -1.0;
  eigtol = -1.0;

  /*
   * nag_correg_corrmat_target (g02apc).
   * Calculate nearest correlation matrix using target matrix
   */
  nag_correg_corrmat_target(g, pdg, n, theta, h, pdh, errtol, eigtol, x, pdx,
                            &alpha, &iter, &eigmin, &norm, &fail);
  if (fail.code != NE_NOERROR) {
    printf("Error from nag_correg_corrmat_target (g02apc).\n%s\n",
           fail.message);
    exit_status = 1;
    goto END;
  }

  /* Display results */

  order = Nag_ColMajor;
  /*
   * nag_file_print_matrix_real_gen (x04cac).
   * Prints real general matrix
   */
  nag_file_print_matrix_real_gen(order, Nag_GeneralMatrix, Nag_NonUnitDiag, n,
                                 n, g, pdg, "Symmetrised Input Matrix G", NULL,
                                 &fail);
  if (fail.code != NE_NOERROR) {
    printf("Error from nag_file_print_matrix_real_gen (x04cac).\n%s\n",
           fail.message);
    exit_status = 2;
    goto END;
  }

  printf("\n");
  fflush(stdout);
  nag_file_print_matrix_real_gen(order, Nag_GeneralMatrix, Nag_NonUnitDiag, n,
                                 n, x, pdx, "Nearest Correlation Matrix X",
                                 NULL, &fail);
  if (fail.code != NE_NOERROR) {
    printf("Error from nag_file_print_matrix_real_gen (x04cac).\n%s\n",
           fail.message);
    exit_status = 3;
    goto END;
  }

  printf("\n%s %9" NAG_IFMT " \n\n", "Number of iterations taken:", iter);
  printf("%s %34.4f \n\n", "alpha: ", alpha);
  printf("%s %29.4f \n\n", "norm value: ", norm);
  printf("%s %34.4f \n\n", "theta: ", theta);
  printf("%s %15.4f \n", "Smallest eigenvalue of A: ", eigmin);

  /*
   * nag_lapackeig_dsyev (f08fac).
   * Computes all eigenvalues and, optionally, eigenvectors of a real
   * symmetric matrix
   */
  nag_lapackeig_dsyev(order, Nag_EigVals, Nag_Upper, n, x, pdx, eig, &fail);
  if (fail.code != NE_NOERROR) {
    printf("Error from nag_lapackeig_dsyev (f08fac).\n%s\n", fail.message);
    exit_status = 4;
    goto END;
  }

  printf("\n");
  fflush(stdout);
  /*
   * nag_file_print_matrix_real_gen (x04cac).
   * Print real general matrix (easy-to-use)
   */
  nag_file_print_matrix_real_gen(order, Nag_GeneralMatrix, Nag_NonUnitDiag, one,
                                 n, eig, one, "Eigenvalues of x", NULL, &fail);
  if (fail.code != NE_NOERROR) {
    printf("Error from nag_file_print_matrix_real_gen (x04cac).\n%s\n",
           fail.message);
    exit_status = 5;
    goto END;
  }

END:
  NAG_FREE(eig);
  NAG_FREE(g);
  NAG_FREE(h);
  NAG_FREE(x);
  return exit_status;
}