22.2 PLAIN Monte Carlo

E(f; N) = = V <f> = (V / N) \sum_i^N f(x_i).

where V is the volume of the integration region. The error on this estimate \sigma(E;N) is calculated from the estimated variance of the mean,

\sigma^2 (E; N) = (V / N) \sum_i^N (f(x_i) - <f>)^2

For large N this variance decreases asymptotically as var(f)/N, where var(f) is the true variance of the function over the integration region. The error estimate itself should decrease as \sigma(f)/\sqrt{N}. The familiar law of errors decreasing as 1/\sqrt{N} applies -- to reduce the error by a factor of 10 requires a 100-fold increase in the number of sample points.

The functions described in this section are declared in the header file `gsl_monte_plain.h'.

Function: gsl_monte_plain_state * gsl_monte_plain_alloc (size_t dim)

This function allocates and initializes a workspace for Monte Carlo integration in dim dimensions.

Function: int gsl_monte_plain_init (gsl_monte_plain_state* s)

This function initializes a previously allocated integration state. This allows an existing workspace to be reused for different integrations.

Function: int gsl_monte_plain_integrate (gsl_monte_function * f, double * xl, double * xu, size_t dim, size_t calls, gsl_rng * r, gsl_monte_plain_state * s, double * result, double * abserr)

This routines uses the plain Monte Carlo algorithm to integrate the function f over the dim-dimensional hypercubic region defined by the lower and upper limits in the arrays xl and xu, each of size dim. The integration uses a fixed number of function calls calls, and obtains random sampling points using the random number generator r. A previously allocated workspace s must be supplied. The result of the integration is returned in result, with an estimated absolute error abserr.

Function: void gsl_monte_plain_free (gsl_monte_plain_state* s),

This function frees the memory associated with the integrator state s.