## Examples

This example program finds the minimum of the paraboloid function defined earlier. The location of the minimum is offset from the origin in @math{x} and @math{y}, and the function value at the minimum is non-zero. The main program is given below, it requires the example function given earlier in this chapter.

```int
main (void)
{
size_t iter = 0;
int status;

const gsl_multimin_fdfminimizer_type *T;
gsl_multimin_fdfminimizer *s;

/* Position of the minimum (1,2). */
double par[2] = { 1.0, 2.0 };

gsl_vector *x;
gsl_multimin_function_fdf my_func;

my_func.f = &my_f;
my_func.df = &my_df;
my_func.fdf = &my_fdf;
my_func.n = 2;
my_func.params = &par;

/* Starting point, x = (5,7) */

x = gsl_vector_alloc (2);
gsl_vector_set (x, 0, 5.0);
gsl_vector_set (x, 1, 7.0);

T = gsl_multimin_fdfminimizer_conjugate_fr;
s = gsl_multimin_fdfminimizer_alloc (T, 2);

gsl_multimin_fdfminimizer_set (s, &my_func, x, 0.01, 1e-4);

do
{
iter++;
status = gsl_multimin_fdfminimizer_iterate (s);

if (status)
break;

if (status == GSL_SUCCESS)
printf ("Minimum found at:\n");

printf ("%5d %.5f %.5f %10.5f\n", iter,
gsl_vector_get (s->x, 0),
gsl_vector_get (s->x, 1),
s->f);

}
while (status == GSL_CONTINUE && iter < 100);

gsl_multimin_fdfminimizer_free (s);
gsl_vector_free (x);

return 0;
}
```

The initial step-size is chosen as 0.01, a conservative estimate in this case, and the line minimization parameter is set at 0.0001. The program terminates when the norm of the gradient has been reduced below 0.001. The output of the program is shown below,

```         x       y         f
1 4.99629 6.99072  687.84780
2 4.98886 6.97215  683.55456
3 4.97400 6.93501  675.01278
4 4.94429 6.86073  658.10798
5 4.88487 6.71217  625.01340
6 4.76602 6.41506  561.68440
7 4.52833 5.82083  446.46694
8 4.05295 4.63238  261.79422
9 3.10219 2.25548   75.49762
10 2.85185 1.62963   67.03704
11 2.19088 1.76182   45.31640
12 0.86892 2.02622   30.18555
Minimum found at:
13 1.00000 2.00000   30.00000
```

Note that the algorithm gradually increases the step size as it successfully moves downhill, as can be seen by plotting the successive points.

@image{multimin,4in} The conjugate gradient algorithm finds the minimum on its second direction because the function is purely quadratic. Additional iterations would be needed for a more complicated function.