NAG Library Manual, Mark 27.3
```/* nag_lapackeig_dstev (f08jac) Example Program.
*
* Copyright 2021 Numerical Algorithms Group.
*
* Mark 27.3, 2021.
*/

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

int main(void) {
/* Scalars */
double eerrbd, eps;
Integer exit_status = 0, i, j, n, pdz;
/* Arrays */
double *d = 0, *e = 0, *rcondz = 0, *z = 0, *zerrbd = 0;
/* Nag Types */
Nag_OrderType order;
NagError fail;

#ifdef NAG_COLUMN_MAJOR
#define Z(I, J) z[(J - 1) * pdz + I - 1]
order = Nag_ColMajor;
#else
#define Z(I, J) z[(I - 1) * pdz + J - 1]
order = Nag_RowMajor;
#endif

INIT_FAIL(fail);

printf("nag_lapackeig_dstev (f08jac) Example Program Results\n\n");

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

/* Allocate memory */
if (!(d = NAG_ALLOC(n, double)) || !(e = NAG_ALLOC(n, double)) ||
!(rcondz = NAG_ALLOC(n, double)) || !(z = NAG_ALLOC(n * n, double)) ||
!(zerrbd = NAG_ALLOC(n, double))) {
printf("Allocation failure\n");
exit_status = -1;
goto END;
}

pdz = n;
/* Read the diagonal and off-diagonal elements of the matrix A
* from data file.
*/
for (i = 0; i < n; ++i)
scanf("%lf", &d[i]);
scanf("%*[^\n]");

for (i = 0; i < n - 1; ++i)
scanf("%lf", &e[i]);
scanf("%*[^\n]");

/* nag_lapackeig_dstev (f08jac).
* Solve the symmetric tridiagonal eigenvalue problem.
*/
nag_lapackeig_dstev(order, Nag_DoBoth, n, d, e, z, pdz, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_lapackeig_dstev (f08jac).\n%s\n", fail.message);
exit_status = 1;
goto END;
}

/* Normalize the eigenvectors */
for (j = 1; j <= n; j++)
for (i = n; i >= 1; i--)
Z(i, j) = Z(i, j) / Z(1, j);

/* Print solution */
printf("Eigenvalues\n");
for (i = 0; i < n; ++i)
printf("%8.4f%s", d[i], (i + 1) % 8 == 0 ? "\n" : " ");
printf("\n");

/* nag_file_print_matrix_real_gen (x04cac).
* Print eigenvectors.
*/
fflush(stdout);
nag_file_print_matrix_real_gen(order, Nag_GeneralMatrix, Nag_NonUnitDiag, n,
n, z, pdz, "Eigenvectors", 0, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_file_print_matrix_real_gen (x04cac).\n%s\n",
fail.message);
exit_status = 1;
goto END;
}

/* Get the machine precision, eps, using nag_machine_precision (X02AJC)
* and compute the approximate error bound for the computed eigenvalues.
* Note that for the 2-norm, ||A|| = max {|d[i]|, i=0..n-1}, and since
* the eigenvalues are in ascending order ||A|| = max( |d[0]|, |d[n-1]|).
*/
eps = nag_machine_precision;
eerrbd = eps * MAX(fabs(d[0]), fabs(d[n - 1]));

/* nag_lapackeig_ddisna (f08flc).
* Estimate reciprocal condition numbers for the eigenvectors.
*/
nag_lapackeig_ddisna(Nag_EigVecs, n, n, d, rcondz, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_lapackeig_ddisna (f08flc).\n%s\n", fail.message);
exit_status = 1;
goto END;
}

/* Compute the error estimates for the eigenvectors */
for (i = 0; i < n; ++i)
zerrbd[i] = eerrbd / rcondz[i];

/* Print the approximate error bounds for the eigenvalues and vectors */
printf("\nError estimate for the eigenvalues\n");
printf("%11.1e\n", eerrbd);
printf("\nError estimates for the eigenvectors\n");
for (i = 0; i < n; ++i)
printf("%11.1e%s", zerrbd[i], (i + 1) % 6 == 0 || i == n - 1 ? "\n" : " ");

END:
NAG_FREE(d);
NAG_FREE(e);
NAG_FREE(rcondz);
NAG_FREE(z);
NAG_FREE(zerrbd);

return exit_status;
}

#undef Z
```