## Examples

The example program below uses the Monte Carlo routines to estimate the value of the following 3-dimensional integral from the theory of random walks,

    I = \int_{-pi}^{+pi} {dk_x/(2 pi)}
        \int_{-pi}^{+pi} {dk_y/(2 pi)}
        \int_{-pi}^{+pi} {dk_z/(2 pi)}
         1 / (1 - cos(k_x)cos(k_y)cos(k_z))

The analytic value of this integral can be shown to be I = \Gamma(1/4)^4/(4 \pi^3) = 1.393203929685676859.... The integral gives the mean time spent at the origin by a random walk on a body-centered cubic lattice in three dimensions.

For simplicity we will compute the integral over the region (0,0,0) to (\pi,\pi,\pi) and multiply by 8 to obtain the full result. The integral is slowly varying in the middle of the region but has integrable singularities at the corners (0,0,0), (0,\pi,\pi), (\pi,0,\pi) and (\pi,\pi,0). The Monte Carlo routines only select points which are strictly within the integration region and so no special measures are needed to avoid these singularities.

    #include <stdlib.h>
    #include <gsl/gsl_math.h>
    #include <gsl/gsl_monte.h>
    #include <gsl/gsl_monte_plain.h>
    #include <gsl/gsl_monte_miser.h>
    #include <gsl/gsl_monte_vegas.h>

    /* Computation of the integral,

          I = int (dx dy dz)/(2pi)^3  1/(1-cos(x)cos(y)cos(z))

       over (-pi,-pi,-pi) to (+pi, +pi, +pi).  The exact answer
       is Gamma(1/4)^4/(4 pi^3).  This example is taken from
       C.Itzykson, J.M.Drouffe, "Statistical Field Theory -
       Volume 1", Section 1.1, p21, which cites the original
       paper M.L.Glasser, I.J.Zucker, Proc.Natl.Acad.Sci.USA 74
       1800 (1977) */

    /* For simplicity we compute the integral over the region
       (0,0,0) -> (pi,pi,pi) and multiply by 8 */

    double exact = 1.3932039296856768591842462603255;

    double
    g (double *k, size_t dim, void *params)
    {
      double A = 1.0 / (M_PI * M_PI * M_PI);
      return A / (1.0 - cos (k[0]) * cos (k[1]) * cos (k[2]));
    }

    void
    display_results (char *title, double result, double error)
    {
      printf ("%s ==================\n", title);
      printf ("result = % .6f\n", result);
      printf ("sigma  = % .6f\n", error);
      printf ("exact  = % .6f\n", exact);
      printf ("error  = % .6f = %.1g sigma\n", result - exact,
              fabs (result - exact) / error);
    }

    int
    main (void)
    {
      double res, err;

      double xl[3] = { 0, 0, 0 };
      double xu[3] = { M_PI, M_PI, M_PI };

      const gsl_rng_type *T;
      gsl_rng *r;

      gsl_monte_function G = { &g, 3, 0 };

      size_t calls = 500000;

      gsl_rng_env_setup ();

      T = gsl_rng_default;
      r = gsl_rng_alloc (T);

      {
        gsl_monte_plain_state *s = gsl_monte_plain_alloc (3);
        gsl_monte_plain_integrate (&G, xl, xu, 3, calls, r, s,
                                   &res, &err);
        gsl_monte_plain_free (s);

        display_results ("plain", res, err);
      }

      {
        gsl_monte_miser_state *s = gsl_monte_miser_alloc (3);
        gsl_monte_miser_integrate (&G, xl, xu, 3, calls, r, s,
                                   &res, &err);
        gsl_monte_miser_free (s);

        display_results ("miser", res, err);
      }

      {
        gsl_monte_vegas_state *s = gsl_monte_vegas_alloc (3);

        gsl_monte_vegas_integrate (&G, xl, xu, 3, 10000, r, s,
                                   &res, &err);
        display_results ("vegas warm-up", res, err);

        printf ("converging...\n");

        do
          {
            gsl_monte_vegas_integrate (&G, xl, xu, 3, calls/5, r, s,
                                       &res, &err);
            printf ("result = % .6f sigma = % .6f "
                    "chisq/dof = %.1f\n", res, err, s->chisq);
          }
        while (fabs (s->chisq - 1.0) > 0.5);

        display_results ("vegas final", res, err);

        gsl_monte_vegas_free (s);
      }
      return 0;
    }

With 500,000 function calls the plain Monte Carlo algorithm achieves a fractional error of 0.6%. The estimated error `sigma` is consistent with the actual error, and the computed result differs from the true result by about one standard deviation,

    plain ==================
    result =  1.385867
    sigma  =  0.007938
    exact  =  1.393204
    error  = -0.007337 = 0.9 sigma

The MISER algorithm reduces the error by a factor of two, and also correctly estimates the error,

    miser ==================
    result =  1.390656
    sigma  =  0.003743
    exact  =  1.393204
    error  = -0.002548 = 0.7 sigma

In the case of the VEGAS algorithm the program uses an initial warm-up run of 10,000 function calls to prepare, or "warm up", the grid. This is followed by a main run with five iterations of 100,000 function calls. The chi-squared per degree of freedom for the five iterations are checked for consistency with 1, and the run is repeated if the results have not converged. In this case the estimates are consistent on the first pass.

    vegas warm-up ==================
    result =  1.386925
    sigma  =  0.002651
    exact  =  1.393204
    error  = -0.006278 = 2 sigma
    converging...
    result =  1.392957 sigma =  0.000452 chisq/dof = 1.1
    vegas final ==================
    result =  1.392957
    sigma  =  0.000452
    exact  =  1.393204
    error  = -0.000247 = 0.5 sigma

If the value of `chisq` had differed significantly from 1 it would indicate inconsistent results, with a correspondingly underestimated error. The final estimate from VEGAS (using a similar number of function calls) is significantly more accurate than the other two algorithms.